Prognostic Significance of Inflammatory Marker Combinations for Clinicopathological Features in Endometrial Cancer

Prognostic Significance of Inflammatory Marker Combinations for Clinicopathological Features in Endometrial Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Significance of Inflammatory Marker Combinations for Clinicopathological Features in Endometrial Cancer Kasim AKAY, Gorkem ULGER, Hamza YILDIZ, Zeynep KUCUKOLCAY COSKUN, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7054950/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 Background The role of systemic inflammation in cancer prognosis has garnered increasing interest. The prognostic significance of novel inflammatory markers such as glucose to lymphocyte ratio (GLR) in endometrioid-type endometrial cancer (EC) and their relationship with clinicopathological parameters have not yet been fully elucidated. This study aimed to evaluate the association of preoperative inflammatory markers, particularly with lymphovascular space invasion (LVSI), and aggressive tumor characteristics, and to develop a model predicting LVSI presence. Methods Data from 156 patients who underwent surgical treatment for endometrioid-type EC were retrospectively analysed. Optimal threshold values for inflammatory markers such as GLR, platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), and Systemic Immune-Inflammation Index (SII) calculated from preoperative blood tests in predicting LVSI were determined using ROC analysis. Multivariate logistic regression analysis was used to identify independent risk factors, and a nomogram predicting LVSI was created. Results High PLR (OR: 3.70, p = 0.033) and high NLR (OR: 8.36, p = 0.044) values were identified as independent risk factors for LVSI. High GLR showed a strong trend towards an increased LVSI risk (OR: 4.49, p = 0.054). SII, which showed the strongest association with LVSI in univariate analysis (p = 0.004), could not be included in multivariate analysis due to statistical instability in the model. A nomogram based on PLR, NLR, and GLR was successfully developed to predict LVSI. Conclusions Preoperative inflammatory markers are valuable, low-cost, and easily accessible tools for predicting LVSI presence in endometrioid-type EC. The developed nomogram may assist clinicians in identifying high-risk patients and personalizing surgical strategy. Trial registration: Retrospectively registered. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (No: 2024/52, Date: 03.06.2024). Glucose to lymphocyte ratio endometrial cancer lymphovascular space invasion nomogram prognostic biomarker Figures Figure 1 Figure 2 Figure 3 Background Endometrial cancer (EC) ranks among the most common gynecological malignancies affecting women in developed countries, with its incidence steadily increasing ( 1 , 2 ). Endometrioid-type EC constitutes approximately 75–80% of all endometrial cancers and generally has a better prognosis; however, its heterogeneous nature complicates prognostic assessment and treatment planning ( 3 , 4 ). In recent years, the critical role of systemic inflammation in the tumor microenvironment and its impact on cancer prognosis is an area of growing investigation ( 5 , 6 ). The inflammatory response contributes to tumorigenesis by causing DNA damage, stimulating angiogenesis, and potentiating pro-proliferative and anti-apoptotic processes ( 7 ). In this context, systemic inflammatory markers such as neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and Systemic Immune-Inflammation Index (SII) have emerged as biomarkers with prognostic value in various cancer types ( 8 , 9 ). Recently, new inflammatory indices including glucose to lymphocyte ratio (GLR) have shown promising results in cancer prognosis ( 10 , 11 ). One of the most significant innovations of the 2023 FIGO staging system is the more prominent inclusion of lymphovascular space invasion (LVSI) assessment in the system ( 12 , 13 ). LVSI, characterized by tumor cell invasion into blood and lymphatic vessels, is recognized as a prognostic indicator closely associated with tumor aggressiveness, lymph node metastasis risk, and recurrence rates ( 14 , 15 ). The more detailed evaluation of LVSI in the 2023 FIGO staging system indicates that this parameter has evolved from being merely a prognostic indicator to a factor directly influencing staging and treatment decisions. This development underscores the growing clinical need for accurate preoperative prediction of LVSI. In this context, the potential of routinely measurable preoperative inflammatory markers to predict aggressive tumor characteristics, particularly LVSI, and the development of predictive models based on these markers could provide significant contributions to clinical practice. Such an approach offers a cost-effective and readily implementable tool for risk stratification. Methods This retrospective cohort study encompasses patients who underwent surgical treatment for endometrioid-type EC at Mersin University Hospital Department of Obstetrics and Gynecology between January 2019 and December 2023. Approval was obtained from Mersin University Ethics Committee for the study (Approval number: 52, Date: 03.06.2024). Inclusion criteria were defined as: patients aged 18 years and older, operated for endometrioid-type EC, and who had not previously undergone surgical intervention for EC. Exclusion criteria included: patients previously operated for EC, patients with non-endometrioid type EC, patients with a history of chemotherapy or radiotherapy, patients with insufficient medical records, patients under 18 years of age, patients with a previous history of malignancy (n = 14), patients with autoimmune disease (n = 6), patients with endometriosis (n = 2), patients using steroid medication (n = 4), and patients who had COVID-19 disease within the preoperative 2-month period (n = 20). Of the 202 patients initially evaluated, 156 were included in the final analysis (Fig. 1 ). Standard staging surgery was performed on all patients. The basic surgical procedure included total abdominal hysterectomy + bilateral salpingo-oophorectomy (TAH + BSO) or laparoscopic hysterectomy + bilateral salpingo-oophorectomy. Peritoneal cytology samples were routinely obtained from all patients. Tumor size, degree of myometrial invasion, and tumor grade were evaluated with intraoperative frozen section analysis. Pelvic lymph node dissection was performed without waiting for frozen section results in patients whose tumor diameter was determined to be greater than 2 cm on preoperative imaging. In patients in the high-risk group according to frozen section results, paraaortic lymph node dissection was also performed in addition to pelvic lymph node dissection. Patients' demographic characteristics, gravidity, parity numbers, comorbidities, and malignancy histories were recorded. Complete blood count (hemoglobin, hematocrit, leukocyte, neutrophil, lymphocyte, platelet, monocyte counts), fasting blood glucose, and serum CA125 levels were obtained from routine laboratory tests taken in the preoperative period (within the last week before surgery). Inflammatory markers were calculated from preoperative laboratory values: Platelet to Lymphocyte Ratio (PLR): Platelet count / Lymphocyte count, Neutrophil to Lymphocyte Ratio (NLR): Neutrophil count / Lymphocyte count, Systemic Immune-Inflammation Index (SII): (Platelet count × Neutrophil count) / Lymphocyte count, Glucose to Lymphocyte Ratio (GLR): Fasting blood glucose / Lymphocyte count. Data obtained from postoperative pathology examination included: tumor type and size, histological grade (Grade 1, 2, 3 according to FIGO system), presence of lymphovascular space invasion (LVSI), presence and degree of myometrial invasion, presence of cervical stromal invasion, lymph node metastasis status, presence of distant organ metastasis, and FIGO staging. LVSI assessment was performed only on final hysterectomy specimens due to limited tissue sampling and potential sampling error found in preoperative biopsies. Statistical analyses were performed using IBM SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and MedCalc Statistical Software version 20.211 (MedCalc Software, Ostend, Belgium) for ROC analyses. The nomogram was created using R programming language (version 4.4.0) and RStudio interface with the "rms" package. Statistical significance level was accepted as p < 0.05 and all data were evaluated at 95% confidence interval. The normality of data distribution was assessed using the Kolmogorov-Smirnov test. Continuous variables with normal distribution were presented as mean ± standard deviation, variables without normal distribution as median (minimum-maximum), and categorical variables as frequency (percentage). Student's t-test was used for comparing continuous variables with normal distribution, Mann-Whitney U test for comparing continuous variables without normal distribution, and Chi-square test or Fisher's exact test for comparing categorical variables. Optimal cut-off values of inflammatory markers in predicting LVSI presence were determined by ROC analysis using the Youden index (sensitivity + specificity − 1). To determine risk factors for LVSI presence, univariate logistic regression analysis was performed and odds ratio (OR) and 95% confidence interval were calculated for each inflammatory marker. All parameters with p < 0.05 in univariate analysis were included in multivariate logistic regression analysis to determine independent risk factors. In multivariate logistic regression analysis, the SII variable was excluded from the final multivariate model due to quasi-complete separation, a form of statistical instability that arose because all LVSI-positive patients also had high SII values. This was due to all 17 LVSI-positive patients also being in the high SII group. The final prediction model and nomogram were created using three markers found to be statistically significant (PLR, NLR, and GLR). The power of the study was calculated post-hoc and it was determined that the study had sufficient sample size for α = 0.05 and β = 0.20 (power = 80%) with an LVSI positivity rate of 10.9% (17/156). Results A total of 156 patients with endometrioid-type EC were included in the study. The basic demographic and clinical characteristics of patients are presented in Table 1 . The mean age of patients was 60.01 ± 9.31 years. Of the patients, 118 (75.6%) were 55 years and older, while 38 (24.4%) were under 55 years. When demographic parameters were compared between LVSI positive and negative patient groups; the mean age was 58.06 ± 10.23 years in LVSI positive patients and 60.24 ± 9.21 years in LVSI negative patients (p = 0.363). No significant difference was found between groups in terms of gravidity [3.0 (0–7.0) vs 2.0 (0–11.0), p = 0.497] and parity [3.0 (0–7.0) vs 2.0 (0–11.0), p = 0.187] (Table 1 ). Tumor size was significantly larger in LVSI positive patients [5.50 cm (2.0–8.0) vs 3.0 cm (0.30–9.50), p < 0.001]. Hemoglobin value was significantly lower in LVSI positive patients [12.0 g/dL (8.30–13.70) vs 13.0 g/dL (1.70–16.70), p = 0.002], while platelet count was significantly higher [306.0 x10³/µL (210.0-530.0) vs 281.0 x10³/µL (84.0-470.0), p = 0.036] (Table 1 ). Serum CA125 level was found to be significantly high in LVSI positive patients [25.90 U/mL (5.60-197.9) vs 15.20 U/mL (4.70-274.80), p = 0.030]. No significant difference was found between groups in terms of other laboratory parameters (leukocyte, neutrophil, lymphocyte, monocyte counts and glucose levels) (p > 0.05 for all) (Table 1 ). Table 1 Basic characteristics of the study population according to LVSI status Demographic Data and Laboratory Parameters LVSI(-) LVSI(+) P-value Age (year) 60.24 ± 9.21 58.06 ± 10.23 0.363 Gravidity 2.0 (0–11.0) 3.0 (0–7.0) 0.497 Parity 2.0 (0–11.0) 3.0 (0–7.0) 0.187 Tumor size (cm) 3.0 (0.30–9.50) 5.50 (2.0–8.0) < 0.001 Leukocytes (x 10³/µL) 8.38 ± 2.49 8.54 ± 2.41 0.808 Hemoglobin (g/dL) 13.0 (8.10–16.70) 12.0 (8.30–13.70) 0.002* Platelets (x 10³/µL) 281.0 (84.0-470.0) 306.0 (210.0-530.0) 0.036* Neutrophil (x 10³/µL) 5.0 (0.92–11.05) 6.15 (2.51–8.99) 0.455 Lymphocytes(x 10³/µL) 2.17 (0.74–6.10) 2.22 (0.75–3.54) 0.566 Monocytes (x 10³/µL) 0.55 (0.24–1.12) 0.56 (0.25–0.89) 0.324 Glucose (mg/dL) 107.60 (71.30-398.40) 116.0 (87.0-228.0) 0.096 CA125 15.20 (4.70-274.80) 25.90 (5.60-197.9) 0.030* PLR 123.96 (60.09-272.19) 168.25 (88.68–324.0) 0.007* NLR 2.28 (0.29–7.87) 2.54 (1.67–9.73) 0.163 SII 614.69 (143.11-1984.40) 783.93 (507.05-2365.20) 0.012* GLR 48.90 (6.27-244.42) 53.13 (27.97–304.0) 0.137 ROC Analysis and Prediction Performance The effectiveness of inflammatory markers in predicting LVSI was evaluated with ROC analysis. The highest prediction value was calculated as AUC 0.892 (95% CI: 0.834–0.950) for SII. AUC values of other markers were found as: PLR 0.647 (95% CI: 0.553–0.742), NLR 0.655 (95% CI: 0.559–0.751), GLR 0.608 (95% CI: 0.508–0.708) (Fig. 2 ). Relationship Between Inflammatory Markers and Clinicopathological Parameters The PLR cut-off value was determined as 130.24 (Fig. 2 ). Clinicopathological features were compared between patients with PLR ≥ 130.24 (n = 74) and patients with PLR < 130.24 (n = 82) (Table 2 ). In age distribution, the proportion of patients under 55 years was higher in the high PLR group (23 vs 15 patients, p = 0.063). There was no significant difference between groups in terms of histological grade (p = 0.581). Regarding tumor stage, the rate of Stage IIA and above disease was numerically higher in the high PLR group but did not reach statistical significance (22 vs 16 patients, p = 0.138). LVSI positivity was significantly more frequent in patients with PLR ≥ 130.24 (13 vs 4 patients, p = 0.011). There was no difference between groups in terms of stromal invasion (p = 0.228). Lymph node metastasis was significantly more common in the high PLR group (9 vs 3 patients, p = 0.043). No difference was found between groups in terms of distant organ metastasis (p = 0.599). The NLR cut-off value was determined as 1.83 (Fig. 2 ). The number of patients with NLR ≥ 1.83 was 109 (69.9%), and the number of patients with NLR < 1.83 was 47 (30.1%). Age and histological grade distribution were similar in the high NLR group (p = 0.556 and p = 0.535, respectively). Regarding tumor stage, the rate of Stage IIA and above disease was higher in patients with NLR ≥ 1.83 but did not reach statistical significance (30 vs 8 patients, p = 0.161). LVSI positivity was found significantly more frequently in patients with NLR ≥ 1.83 (16 vs 1 patient, p = 0.021). There was no difference between groups in terms of stromal invasion (p = 0.114). No significant difference was found in terms of lymph node metastasis (p = 0.687). Distant organ metastasis was significantly more common in the high NLR group (7 vs 0 patients, p = 0.023). The SII cut-off value was determined as 506.38 (Fig. 2 ). The number of patients with SII ≥ 506.38 was 108 (69.2%), and the number of patients with SII < 506.38 was 48 (30.8%). Histological grade distribution was significantly different in the high SII group; Grade 1 tumor rate was higher in the low SII group, while Grade 2–3 tumor rate was higher in the high SII group (p = 0.045). LVSI positivity was significantly more common in patients with SII ≥ 506.38 (17 vs 0 patients, p = 0.004). There was no difference between groups in terms of stromal invasion (p = 0.264). Lymph node metastasis was numerically more common in the high SII group but did not reach statistical significance (11 vs 1 patient, p = 0.080). Distant organ metastasis was found significantly more frequently in the high SII group (7 vs 0 patients, p = 0.021). The GLR cut-off value was determined as 44.61 (Fig. 2 ). The number of patients with GLR ≥ 44.61 was 99 (63.5%), and the number of patients with GLR < 44.61 was 57 (36.5%). The proportion of patients with tumor size ≥ 2 cm was significantly higher in the high GLR group (74 vs 51 patients, p = 0.026). There was no significant difference between groups in terms of histological grade and tumor stage (p = 0.866 and p = 0.058, respectively). LVSI positivity was significantly more frequent in patients with GLR ≥ 44.61 (15 vs 2 patients, p = 0.025). Stromal invasion was also significantly more common in this group (17 vs 3 patients, p = 0.032). No significant difference was found between groups in terms of lymph node metastasis and distant organ metastasis (p = 0.810 and p = 0.211, respectively). Table 2 Relationship between preoperative inflammatory markers and clinicopathological features PLR NLR SII GLR Parameter < 130.24 n = 82 ≥ 130.24 n = 74 p < 1.83 n = 47 ≥ 1.83 n = 109 p < 506.38 n = 48 ≥ 506.38 n = 108 p < 44.61 n = 57 ≥ 44.61 n = 99 p Age, n < 55 years 23 0.063 10 28 0.556 7 31 0.058 12 26 0.465 ≥ 55 years 67 51 37 81 41 77 45 73 Histologic grade, n Grade 1 61 53 0.581 37 77 0.535 41 73 0.045* 41 73 0.866 Grade 2 12 15 7 20 4 23 11 16 Grade 3 9 6 3 12 3 12 5 10 Tumor stages Stage IA1,IA2,IA3,IB,IC 66 52 0.138 39 79 0.161 41 77 0.054 48 70 0.058 Stage IIA and higher 16 22 8 30 7 31 9 29 Tumor size, n < 2 cm 16 15 0.906 8 23 0.558 11 20 0.525 6 25 0.026* ≥ 2 cm 66 59 39 86 37 88 51 74 LVSI, n No 78 61 0.011* 46 93 0.021* 48 91 0.004* 55 84 0.025* Yes 4 13 1 16 0 7 2 15 Stromal invasion, n No 74 62 0.228 44 92 0.114 44 92 0.264 54 82 0.032* Yes 8 12 3 17 4 16 3 17 LNM, n No 79 65 0.043* 44 100 0.687 47 97 0.080 53 91 0.810 Yes 3 9 3 9 1 11 4 8 Distant Organ Metastases, n No 79 70 0.599 47 102 0.023* 48 101 0.021 * 56 93 0.211 Yes 3 4 0 7 0 7 1 6 Development of Prediction Model for LVSI In univariate logistic regression analysis, factors associated with LVSI were identified as: PLR ≥ 130.24 (p = 0.011), NLR ≥ 1.83 (p = 0.021), SII ≥ 506.38 (p = 0.004), and GLR ≥ 44.61 (p = 0.025). In multivariate logistic regression analysis performed to predict LVSI presence, high PLR (OR: 3.70, 95% CI: 1.11–12.31, p = 0.033), high NLR (OR: 8.36, 95% CI: 1.06–65.88, p = 0.044), and high GLR (OR: 4.49, 95% CI: 0.97–20.79, p = 0.054) were found to increase LVSI risk (Table 3 ). Table 3 Multivariate logistic regression analysis results for the presence of Lymphovascular Space Invasion (LVSI) Variable Odds Ratio (OR) 95% Confidence Interval (CI) P-value PLR ≥ 130.24 3.7 1.11–12.31 0.033 NLR ≥ 1.83 8.36 1.06–65.88 0.044 GLR ≥ 44.61 4.49 0.97–20.79 0.054 A prediction nomogram based on these three independent risk factors was created (Fig. 3 ). The nomogram presented in Fig. 3 is designed for practical clinical use to estimate the preoperative risk of LVSI. To utilize the nomogram, a clinician first determines a patient's status for each of the three variables: PLR, NLR, and GLR, based on the established cut-off values (PLR ≥ 130.24, NLR ≥ 1.83, and GLR ≥ 44.61, respectively). For each variable, the corresponding point value is identified by drawing a vertical line from the patient’s category on the variable's axis up to the 'Points' axis at the top. The points for all three variables are then summed to obtain a 'Total Points' score. Finally, this total score is located on the 'Total Points' axis, and another vertical line is projected downwards to the 'Risk of LVSI' axis to determine the final predicted probability of LVSI for that patient. Discussion It has been reported that preoperative inflammation markers are associated with clinical features in endometrioid-type EC before; however, the role of novel GLR has not been evaluated before. In this study it was shown that preoperative inflammatory markers; GLR, PLR, and NLR are associated with LVSI in endometrioid EC. In addition, a new, practical, 3-variable nomogram from these markers was developed for preoperative prediction of LVSI for the first time. GLR has emerged as a novel biomarker whose prognostic significance has been investigated in various solid tumors in recent years. In our study, high GLR values were found to be significantly associated with LVSI in univariate analysis (p = 0.025). However, in the multivariate model containing other strong inflammatory markers such as PLR and NLR, the role of GLR as an independent predictive factor remained at the border of statistical significance (p = 0.054). This fundamental relationship between GLR and LVSI, an aggressive tumor characteristic, aligns with findings from studies emphasizing its prognostic importance in other solid tumors such as pancreatic, lung, and breast cancer ( 10 , 11 , 16 ). Studies in gastric, pancreatic and lung cancer patients have shown that high GLR values are associated with aggressive tumor characteristics and poor overall survival ( 11 , 16 , 17 ) The prognostic value of GLR stems from its composite structure based on the combined evaluation of glucose metabolism and systemic immune status. A particularly noteworthy finding is that while glucose (p = 0.096) and lymphocyte (p = 0.566) levels alone showed no significant relationship with LVSI presence in our study, GLR, which is the ratio of these two values, emerged as a significant marker (p = 0.025). This outcome underscores the utility of composite markers, which can reveal prognostic relationships that are not apparent when their individual components are analyzed alone. While high glucose levels increase tumor cell growth and invasive capacity, low lymphocyte counts indicate suppression of immune response, and the combination of these two factors is associated with aggressive tumor behaviour ( 17 , 18 ). The prognostic role of GLR in endometrioid EC can be explained by the effect of hyperglycemia on the tumor microenvironment and lymphocytopenia's suppression of anti-tumor immune response. This is also supported by the known relationship between glucose metabolism and EC risk. The meta-analysis study by Galeone et al. showed that high glycemic load increases EC risk ( 19 ). This suggests that hyperglycemia may promote endometrial carcinogenesis through insulin and insulin-like growth factors. In our study, the finding that GLR is associated with aggressive tumor characteristics such as LVSI, deep myometrial invasion, and lymph node metastasis indicates that glucose metabolism disorder may be effective not only in cancer development but also in aggressive tumor behavior. In our study, significantly more aggressive clinicopathological features were detected in patients with PLR ≥ 130.24 compared to patients with low PLR values. Our finding in EC is consistent with the existing literature. In the study by Cummings et al. including 605 EC patients, high PLR values were found to show significant correlation with advanced FIGO stage, presence of LVSI, and lymph node positivity ( 20 ). Similarly, another study conducted in non-endometrioid endometrial cancers showed that PLR is associated with disease aggressiveness ( 21 ). These findings suggest that systemic inflammatory markers have prognostic value in different histological subtypes of EC. The comprehensive meta-analysis by Ni et al. including 9 studies and 3390 EC provides strong evidence for the prognostic value of PLR in EC ( 22 ). This meta-analysis showed the negative effect of high PLR values on both overall survival (pHR = 1.99, 95% CI 1.51–2.61) and disease-free survival (pHR = 2.02, 95% CI 1.45–2.80). More importantly, subgroup analyses found that the prognostic value of PLR was consistently maintained at different cut-off values (≤ 190.78 and > 190.78) and that this relationship was valid in different analysis methods (univariate and multivariate). These findings strongly support that PLR is a reliable and reproducible prognostic marker in EC. Our study showed that high NLR values are significantly associated with LVSI positivity (p < 0.05). This finding supports that systemic inflammation is closely related to tumor aggressiveness in EC. The relationship of NLR with LVSI emerges as an important prognostic indicator in EC literature. In a study on non-endometrioid EC, LVSI positivity was found to be significantly higher in the high NLR group Muangto et al. also showed that NLR is statistically significantly associated with myometrial invasion depth in EC, reporting that NLR ≥ 1.93 predicts more than half myometrial invasion with 83.3% sensitivity and 52.8% specificity ( 23 ). Particularly noteworthy is that NLR shows different cut-off values in different cancer types. While values above 3 are generally recommended in meta-analyses for solid tumors, lower values such as 1.93 can also be found significant in EC ( 23 ). This situation reflects cancer type-specific biological differences and variability in the inflammatory profile of the tumor microenvironment. The underlying mechanisms of NLR's relationship with lymphovascular invasion are complex and multifaceted. As stated in the review by Mosca et al., high NLR values were found to be associated with increased peritumoral macrophage infiltration and high levels of various pro-inflammatory cytokines including IL-1ra, IL-6, IL-7, IL-8, IL-12, IL-17, MCP-1, and PDGF-BB. This inflammatory microenvironment facilitates LVSI development by increasing the invasion capacity of tumor cells into vascular and lymphatic structures. Neutrophils can exhibit two different phenotypes in the tumor microenvironment according to their polarization: N1 (anti-tumor) and N2 (pro-tumor) ( 24 ). N2 neutrophils support the tumor invasion process through neo-angiogenesis induction, stroma remodeling, and extracellular matrix degradation, while also inhibiting T cell proliferation. Particularly, neutrophil-derived enzymes such as neutrophil elastase (NE) and metalloproteinase 9 (MMP9) increase the invasion ability of tumor cells into vascular structures by providing degradation of the extracellular matrix. In our study, the SII cut-off value was determined as 506.38, and patients with SII ≥ 506.38 were found to show aggressive clinicopathological features. The high SII group was characterized by significantly higher histological grade (Grade 2–3 rate 32.4% vs 14.6%, p = 0.045), LVSI positivity (17 vs 0 patients, p = 0.004), and distant organ metastasis (7 vs 0 patients, p = 0.021). Although SII demonstrated the strongest univariate association with LVSI, it was excluded from the final multivariate model due to the statistical instability (quasi-complete separation) it introduced. This may be due to all 17 LVSI positive patients in our study also being in the high SII group and creating near-perfect separation in the model. This situation may particularly stem from the imbalance between LVSI positive and negative groups in the sample. In the comprehensive series by Matsubara et al. including 442 patients, the SII cut-off value was determined as 931, and the high SII group constituted 30.5% of patients ( 25 ). In this study, high SII showed strong correlation with advanced FIGO stage (p < 0.001), non-endometrioid histology (p = 0.029), high tumor grade (p < 0.001), LVSI positivity (p = 0.001), and positive peritoneal cytology (p < 0.001). Similarly, in our study, LVSI positivity was found significantly more frequently in patients with SII ≥ 506.38 (17 vs 0 patients, p = 0.004). A prospective study including 522 EC patients examined in detail the relationship between pre-treatment inflammatory markers and clinicopathological features ( 5 ). This study showed that NLR and SII are associated with adverse clinicopathological factors. Particularly noteworthy is that the prognostic value of inflammatory markers was preserved despite 67.2% of patients having low grade and 85.4% having early stage disease. Ji and Wang's systematic meta-analysis comprehensively evaluated the prognostic importance of SII in gynecological and breast cancers ( 26 ). This meta-analysis including 9 articles and 2724 patients proved that high SII is associated with poor overall survival (HR = 2.12, 95% CI, 1.61–2.79, P < 0.001), disease-free survival (HR = 2.28, 95% CI 1.52–3.41, P < 0.001), and increased lymph node metastasis risk (RR = 1.34, 95% CI 1.20–1.50, P < 0.001). Subgroup analyses indicated that the prognostic value of SII was particularly prominent in ovarian cancer and triple-negative breast cancer, but limited data were available for EC. The study by Yang et al. in pancreatic cancer constitutes an important example for the development of multifactorial scoring systems including SII ( 27 ). The PIIN (prognostic immune-inflammatory-nutritional) score developed in this study combines NLR, SII, fibrinogen, ALBI score, and PNI parameters. A significant relationship was found with tumor localization (p = 0.003) and postoperative complications (p < 0.001) in patients with PIIN score ≥ 37.2, and it was proven to be an independent prognostic factor in multivariate analysis (HR = 2.171, 95% CI = 1.207–3.906, p = 0.010). This approach inspires the development of similar combined scoring systems in EC. The present study addresses the literature gap on the role of GLR in endometrioid-type EC, a promising biomarker in other solid tumors. Our research successfully demonstrates that preoperative GLR, alongside PLR and NLR, are independent predictive factors for LVSI. The key clinical output of this work is a practical nomogram based on these three accessible and low-cost blood parameters. This novel tool has the potential to be a valuable aid for clinicians, offering a non-invasive method to predict LVSI risk, thereby helping to identify high-risk patients and personalize surgical strategies, such as the decision for lymphadenectomy. The methodological strengths of this study include its homogeneous patient population (consisting of only endometrioid-type EC), the use of standardized histopathological evaluation criteria, and the determination of optimal cut-off values via ROC analysis. However, the study has several limitations. The retrospective, single-center design carries an inherent risk of selection bias and limits the generalizability of our findings. Furthermore, LVSI assessment is a subjective parameter with potential for inter-observer variability. While the patient cohort (n = 156) was sufficient for primary statistical analyses, it was relatively small for detailed subgroup analyses. Other key limitations include the inability to perform survival analysis due to a lack of long-term follow-up data, the potential confounding effects of comorbidities which were not evaluated in detail, and the absence of molecular subtyping (POLE, MSI, p53), as this analysis was not available for the retrospective cohort. Conclusion In conclusion, this study establishes that preoperative inflammatory markers—PLR, NLR, and GLR are clinically valuable tools for predicting LVSI in endometrioid EC. We have developed a practical, 3-variable nomogram from these markers, offering a promising decision-support tool for preoperative risk stratification and the personalization of surgical management. Our findings particularly highlight the need for more detailed investigation into GLR's prognostic role. Future research, including large-scale prospective and multicenter studies, is essential to validate our model's clinical effectiveness. Combining these inflammatory markers with molecular subtyping and investigating their impact on treatment response in longitudinal studies will be critical steps toward advancing personalized medicine in endometrial cancer. Abbreviations ALBI Albumin-Bilirubin Score AUC Area Under the Curve BSO Bilateral Salpingo-Oophorectomy CA125 Cancer Antigen 125 CI Confidence Interval COVID-19 Coronavirus Disease 2019 EC Endometrial Cancer FIGO International Federation of Gynecology and Obstetrics GLR Glucose to Lymphocyte Ratio HR Hazard Ratio IL Interleukin LVSI Lymphovascular Space Invasion MCP-1 Monocyte Chemoattractant Protein-1 MELF Microcystic, Elongated and Fragmented MMP9 Matrix Metalloproteinase 9 MSI Microsatellite Instability NE Neutrophil Elastase NLR Neutrophil to Lymphocyte Ratio OR Odds Ratio PDGF-BB Platelet-Derived Growth Factor-BB PIIN Prognostic Immune-Inflammatory-Nutritional PLR Platelet to Lymphocyte Ratio PNI Prognostic Nutritional Index POLE DNA Polymerase Epsilon ROC Receiver Operating Characteristic RR Relative Risk SII Systemic Immune-Inflammation Index TAH Total Abdominal Hysterectomy Declarations Ethics approval and consent to participate The study was conducted in accordance with the guidelines of the Helsinki Declaration. Written informed consent was obtained from all participants. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (with the ethics committee decision numbered 2024/52 dated 03.06.2024). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions KA, GU and HY developed the concept and were responsible for data collection. TTI and KA planned the study. KA, TTI and ZCK analysed the results. KA, SGG and HA wrote the manuscript text and prepared figures and tables. All authors reviewed the manuscript for important intellectual content and approved the final version. Acknowledgements Not applicable. Authors' information Not applicable. References Li J, Zhao X, Li X, Lin K, Zeng Z, Ning Z, et al. Analysis of the disease burden of malignancies in the female reproductive system in China from 1990 to 2019: an age-period-cohort study and joinpoint analysis. BMJ Open. 2025;15(4):e081511. Xie J, Maguire FB, Hofer BM, Cooley JJP, Chen HA, Parikh-Patel A, et al. Disparities in hysterectomy-corrected endometrial cancer incidence trends by histologic subtype among racial/ethnic groups in California, 2012-2019. Gynecol Oncol. 2025;197:34-42. Pan X, Li J, Liu P, Li J, Zhao M, Wu Y, et al. Global trends in endometrial cancer and metabolic syndrome research: A bibliometric and visualization analysis. Comput Biol Med. 2025;192(Pt B):110362. Habeshian TS, Park SY, Conti D, Wilkens LR, Marchand LL, Setiawan VW. Inflammatory and insulinemic dietary and lifestyle patterns and incidence of endometrial cancer: the multiethnic cohort. Am J Clin Nutr. 2025;121(6):1236-45. Njoku K, Ramchander NC, Wan YL, Barr CE, Crosbie EJ. Pre-treatment inflammatory parameters predict survival from endometrial cancer: A prospective database analysis. Gynecol Oncol. 2022;164(1):146-53. Marin AG, Filipescu AG, Petca RC, Vlădăreanu R, Petca A. Clinical Correlations between Serological Markers and Endometrial Cancer. Cancers (Basel). 2024;16(10). Ma L, Zhang Y, Shao Y, Luo L, Zhou J, Wu J, et al. Prognostic significance of systemic inflammatory response markers NLR, PLR, and MLR in advanced high-risk endometrial cancer following radiotherapy. Am J Cancer Res. 2025;15(3):966-75. Lin H, Zhong W, Zhong L, Que C, Lin X. The inflammatory markers combined with CA125 may predict postoperative survival in endometrial cancer. J Obstet Gynaecol. 2024;44(1):2373937. Ahn JH, Lee SJ, Yoon JH, Park DC, Kim SI. Prognostic value of pretreatment systemic inflammatory markers in patients with stage I endometrial cancer. Int J Med Sci. 2022;19(14):1989-94. Liu R, Shen Y, Cui J, Ma W, Wang J, Chen C, et al. Association between glucose to lymphocyte ratio and prognosis in patients with solid tumors. Front Immunol. 2024;15:1454393. Park SH, Kang IC, Hong SS, Kim HY, Hwang HK, Kang CM. Glucose-to-Lymphocyte Ratio (GLR) as an Independent Prognostic Factor in Patients with Resected Pancreatic Ductal Adenocarcinoma-Cohort Study. Cancers (Basel). 2024;16(10). Palmieri E, Mariani A, Coleman R, Croce S, Hui P, Lax S, et al. The new 2023 endometrial cancer FIGO staging system: balancing innovation with complexity. Int J Gynecol Cancer. 2025:101823. Zhao X, Sun F, Leng N, Zhang X, Zhu Y. The past, present, and future of FIGO staging of endometrial cancer. J Gynecol Oncol. 2025. Song ZX, Leng H, Zhao XY, Zhu L, Liu Y, Liu CR. [Predictive significance of microcystic elongated and fragmented (MELF) growth pattern in the prognosis of no specific molecular profile endometrial endometrioid carcinoma]. Zhonghua Yi Xue Za Zhi. 2025;105(10):745-52. Dubey A, Ahuja S, Zaheer S. Prognostic significance of tumor budding in endometrial cancer: clinicopathological insights. Korean J Clin Oncol. 2025;21(1):9-12. Yang M, Zhang Q, Ge YZ, Tang M, Hu CL, Wang ZW, et al. Prognostic Roles of Glucose to Lymphocyte Ratio and Modified Glasgow Prognosis Score in Patients With Non-small Cell Lung Cancer. Front Nutr. 2022;9:871301. Yıldırım MB, Özkan MB. Prognostic value of preoperative glucose to lymphocytes ratio in patients with resected gastric cancer. Journal of Surgery and Medicine. 2021;5(9):889-93. Zhong A, Cheng CS, Kai J, Lu R, Guo L. Clinical Significance of Glucose to Lymphocyte Ratio (GLR) as a Prognostic Marker for Patients With Pancreatic Cancer. Front Oncol. 2020;10:520330. Galeone C, Augustin LS, Filomeno M, Malerba S, Zucchetto A, Pelucchi C, et al. Dietary glycemic index, glycemic load, and the risk of endometrial cancer: a case-control study and meta-analysis. Eur J Cancer Prev. 2013;22(1):38-45. Cummings M, Merone L, Keeble C, Burland L, Grzelinski M, Sutton K, et al. Preoperative neutrophil:lymphocyte and platelet:lymphocyte ratios predict endometrial cancer survival. Br J Cancer. 2015;113(2):311-20. Song H, Jeong MJ, Cha J, Lee JS, Yoo JG, Song MJ, et al. Preoperative neutrophil-to-lymphocyte, platelet-to-lymphocyte and monocyte-to-lymphocyte ratio as a prognostic factor in non-endometrioid endometrial cancer. Int J Med Sci. 2021;18(16):3712-7. Ni L, Tao J, Xu J, Yuan X, Long Y, Yu N, et al. Prognostic values of pretreatment neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios in endometrial cancer: a systematic review and meta-analysis. Arch Gynecol Obstet. 2020;301(1):251-61. Muangto T, Maireang K, Poomtavorn Y, Thaweekul Y, Punyashthira A, Chantawong N, et al. Study on Preoperative Neutrophil/Lymphocyte (NLR) and Platelet/Lymphocyte Ratio (PLR) as a Predictive Factor in Endometrial Cancer. Asian Pac J Cancer Prev. 2022;23(10):3317-22. Mosca M, Nigro MC, Pagani R, De Giglio A, Di Federico A. Neutrophil-to-Lymphocyte Ratio (NLR) in NSCLC, Gastrointestinal, and Other Solid Tumors: Immunotherapy and Beyond. Biomolecules. 2023;13(12). Matsubara S, Mabuchi S, Takeda Y, Kawahara N, Kobayashi H. Prognostic value of pre-treatment systemic immune-inflammation index in patients with endometrial cancer. PLoS One. 2021;16(5):e0248871. Ji Y, Wang H. Prognostic prediction of systemic immune-inflammation index for patients with gynecological and breast cancers: a meta-analysis. World J Surg Oncol. 2020;18(1):197. Yang J, Zhou H, Li H, Zhao F, Tong K. Nomogram incorporating prognostic immune-inflammatory-nutritional score for survival prediction in pancreatic cancer: a retrospective study. BMC Cancer. 2024;24(1):193. 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-7054950","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499057570,"identity":"d59c7c3a-e68b-406a-9a57-f40c093de7f3","order_by":0,"name":"Kasim AKAY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACCSA+wMCQwMAgf/7jAyCHh494LRIMxgYgLWzEaGGAajEDcwhqkWw//vDQjZo0eYPbDWmVX3PsZNgYmB8+uoFHizRPjsHhnGM5hhvuHDh2W3ZbMtBhbMbGOXi0yDHkMBzOYatg3HAgse225DZmoBYeNmm8WvifPzic86/CfsOBZLZiyW31hLVISyQYHM5ty0nccCONjfHjtsOEtUjOeAPU0peWPPPMGWZpxm3HediYCfhF4nz6488535Jt+473MH78ua3anp+9+eFjfFrgQOEAAwMzD4jFTIxyEJBvYGBg/EGs6lEwCkbBKBhRAAArO0zgrul4UAAAAABJRU5ErkJggg==","orcid":"","institution":"Osmaniye Duzici State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Kasim","middleName":"","lastName":"AKAY","suffix":""},{"id":499057571,"identity":"7fc9318a-d344-4c71-98f8-95c8aa32eb44","order_by":1,"name":"Gorkem ULGER","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Gorkem","middleName":"","lastName":"ULGER","suffix":""},{"id":499057572,"identity":"b1ffb475-f67f-4a05-944b-b894288812b2","order_by":2,"name":"Hamza YILDIZ","email":"","orcid":"","institution":"Mersin Tarsus State Hospital, Kasim Akay","correspondingAuthor":false,"prefix":"","firstName":"Hamza","middleName":"","lastName":"YILDIZ","suffix":""},{"id":499057573,"identity":"00c19723-71e5-4a37-a2fd-ada9b66eefb3","order_by":3,"name":"Zeynep KUCUKOLCAY COSKUN","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Zeynep","middleName":"KUCUKOLCAY","lastName":"COSKUN","suffix":""},{"id":499057574,"identity":"804db6ec-a395-47a6-9857-5a69a1d3a128","order_by":4,"name":"Sevki Goksun GOKULU","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Sevki","middleName":"Goksun","lastName":"GOKULU","suffix":""},{"id":499057575,"identity":"004bfd83-626e-464f-acdc-6e256036b091","order_by":5,"name":"Tolgay Tuyan ILHAN","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Tolgay","middleName":"Tuyan","lastName":"ILHAN","suffix":""},{"id":499057576,"identity":"d848a0f8-6fc3-4615-8c58-071639dddbf0","order_by":6,"name":"Hakan AYTAN","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Hakan","middleName":"","lastName":"AYTAN","suffix":""}],"badges":[],"createdAt":"2025-07-05 21:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7054950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7054950/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88948377,"identity":"7f24eefc-2479-48ef-b9d2-34ddbcebbf20","added_by":"auto","created_at":"2025-08-13 05:34:08","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197744,"visible":true,"origin":"","legend":"\u003cp\u003ePatient Selection Flowchart\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7054950/v1/f11e16c8ef760ff28fec1612.jpeg"},{"id":88948381,"identity":"ca567bcb-8491-4e5e-865b-073680385449","added_by":"auto","created_at":"2025-08-13 05:34:08","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200016,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curves showing the performance of inflammatory markers in predicting Lymphovascular Space Invasion (LVSI)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7054950/v1/d126dca635f3adb682312945.jpeg"},{"id":88949912,"identity":"e80838b3-50ce-45b9-a713-8d93f12068c7","added_by":"auto","created_at":"2025-08-13 05:42:08","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96300,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction nomogram developed based on preoperative PLR, NLR, and GLR values to predict LVSI presence\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7054950/v1/989661e519c17aa7e439d4f0.jpeg"},{"id":104874214,"identity":"160e99f0-5982-4498-a732-3b4b3ee4e7a6","added_by":"auto","created_at":"2026-03-18 08:29:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1448085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7054950/v1/f32d2015-1057-426e-96c3-5c7a9316c7db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Significance of Inflammatory Marker Combinations for Clinicopathological Features in Endometrial Cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eEndometrial cancer (EC) ranks among the most common gynecological malignancies affecting women in developed countries, with its incidence steadily increasing (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Endometrioid-type EC constitutes approximately 75–80% of all endometrial cancers and generally has a better prognosis; however, its heterogeneous nature complicates prognostic assessment and treatment planning (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, the critical role of systemic inflammation in the tumor microenvironment and its impact on cancer prognosis is an area of growing investigation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The inflammatory response contributes to tumorigenesis by causing DNA damage, stimulating angiogenesis, and potentiating pro-proliferative and anti-apoptotic processes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In this context, systemic inflammatory markers such as neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and Systemic Immune-Inflammation Index (SII) have emerged as biomarkers with prognostic value in various cancer types (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Recently, new inflammatory indices including glucose to lymphocyte ratio (GLR) have shown promising results in cancer prognosis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of the most significant innovations of the 2023 FIGO staging system is the more prominent inclusion of lymphovascular space invasion (LVSI) assessment in the system (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). LVSI, characterized by tumor cell invasion into blood and lymphatic vessels, is recognized as a prognostic indicator closely associated with tumor aggressiveness, lymph node metastasis risk, and recurrence rates (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The more detailed evaluation of LVSI in the 2023 FIGO staging system indicates that this parameter has evolved from being merely a prognostic indicator to a factor directly influencing staging and treatment decisions. This development underscores the growing clinical need for accurate preoperative prediction of LVSI.\u003c/p\u003e\u003cp\u003eIn this context, the potential of routinely measurable preoperative inflammatory markers to predict aggressive tumor characteristics, particularly LVSI, and the development of predictive models based on these markers could provide significant contributions to clinical practice. Such an approach offers a cost-effective and readily implementable tool for risk stratification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective cohort study encompasses patients who underwent surgical treatment for endometrioid-type EC at Mersin University Hospital Department of Obstetrics and Gynecology between January 2019 and December 2023. Approval was obtained from Mersin University Ethics Committee for the study (Approval number: 52, Date: 03.06.2024).\u003c/p\u003e\u003cp\u003eInclusion criteria were defined as: patients aged 18 years and older, operated for endometrioid-type EC, and who had not previously undergone surgical intervention for EC. Exclusion criteria included: patients previously operated for EC, patients with non-endometrioid type EC, patients with a history of chemotherapy or radiotherapy, patients with insufficient medical records, patients under 18 years of age, patients with a previous history of malignancy (n = 14), patients with autoimmune disease (n = 6), patients with endometriosis (n = 2), patients using steroid medication (n = 4), and patients who had COVID-19 disease within the preoperative 2-month period (n = 20). Of the 202 patients initially evaluated, 156 were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStandard staging surgery was performed on all patients. The basic surgical procedure included total abdominal hysterectomy + bilateral salpingo-oophorectomy (TAH + BSO) or laparoscopic hysterectomy + bilateral salpingo-oophorectomy. Peritoneal cytology samples were routinely obtained from all patients. Tumor size, degree of myometrial invasion, and tumor grade were evaluated with intraoperative frozen section analysis. Pelvic lymph node dissection was performed without waiting for frozen section results in patients whose tumor diameter was determined to be greater than 2 cm on preoperative imaging. In patients in the high-risk group according to frozen section results, paraaortic lymph node dissection was also performed in addition to pelvic lymph node dissection.\u003c/p\u003e\u003cp\u003ePatients' demographic characteristics, gravidity, parity numbers, comorbidities, and malignancy histories were recorded. Complete blood count (hemoglobin, hematocrit, leukocyte, neutrophil, lymphocyte, platelet, monocyte counts), fasting blood glucose, and serum CA125 levels were obtained from routine laboratory tests taken in the preoperative period (within the last week before surgery). Inflammatory markers were calculated from preoperative laboratory values: Platelet to Lymphocyte Ratio (PLR): Platelet count / Lymphocyte count, Neutrophil to Lymphocyte Ratio (NLR): Neutrophil count / Lymphocyte count, Systemic Immune-Inflammation Index (SII): (Platelet count × Neutrophil count) / Lymphocyte count, Glucose to Lymphocyte Ratio (GLR): Fasting blood glucose / Lymphocyte count.\u003c/p\u003e\u003cp\u003e Data obtained from postoperative pathology examination included: tumor type and size, histological grade (Grade 1, 2, 3 according to FIGO system), presence of lymphovascular space invasion (LVSI), presence and degree of myometrial invasion, presence of cervical stromal invasion, lymph node metastasis status, presence of distant organ metastasis, and FIGO staging. LVSI assessment was performed only on final hysterectomy specimens due to limited tissue sampling and potential sampling error found in preoperative biopsies.\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using IBM SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and MedCalc Statistical Software version 20.211 (MedCalc Software, Ostend, Belgium) for ROC analyses. The nomogram was created using R programming language (version 4.4.0) and RStudio interface with the \"rms\" package. Statistical significance level was accepted as p \u0026lt; 0.05 and all data were evaluated at 95% confidence interval.\u003c/p\u003e\u003cp\u003eThe normality of data distribution was assessed using the Kolmogorov-Smirnov test. Continuous variables with normal distribution were presented as mean ± standard deviation, variables without normal distribution as median (minimum-maximum), and categorical variables as frequency (percentage). Student's t-test was used for comparing continuous variables with normal distribution, Mann-Whitney U test for comparing continuous variables without normal distribution, and Chi-square test or Fisher's exact test for comparing categorical variables.\u003c/p\u003e\u003cp\u003eOptimal cut-off values of inflammatory markers in predicting LVSI presence were determined by ROC analysis using the Youden index (sensitivity + specificity − 1). To determine risk factors for LVSI presence, univariate logistic regression analysis was performed and odds ratio (OR) and 95% confidence interval were calculated for each inflammatory marker. All parameters with p \u0026lt; 0.05 in univariate analysis were included in multivariate logistic regression analysis to determine independent risk factors.\u003c/p\u003e\u003cp\u003eIn multivariate logistic regression analysis, the SII variable was excluded from the final multivariate model due to quasi-complete separation, a form of statistical instability that arose because all LVSI-positive patients also had high SII values. This was due to all 17 LVSI-positive patients also being in the high SII group. The final prediction model and nomogram were created using three markers found to be statistically significant (PLR, NLR, and GLR). The power of the study was calculated post-hoc and it was determined that the study had sufficient sample size for α = 0.05 and β = 0.20 (power = 80%) with an LVSI positivity rate of 10.9% (17/156).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 156 patients with endometrioid-type EC were included in the study. The basic demographic and clinical characteristics of patients are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of patients was 60.01\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31 years. Of the patients, 118 (75.6%) were 55 years and older, while 38 (24.4%) were under 55 years.\u003c/p\u003e\n\u003cp\u003eWhen demographic parameters were compared between LVSI positive and negative patient groups; the mean age was 58.06\u0026thinsp;\u0026plusmn;\u0026thinsp;10.23 years in LVSI positive patients and 60.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21 years in LVSI negative patients (p\u0026thinsp;=\u0026thinsp;0.363). No significant difference was found between groups in terms of gravidity [3.0 (0\u0026ndash;7.0) vs 2.0 (0\u0026ndash;11.0), p\u0026thinsp;=\u0026thinsp;0.497] and parity [3.0 (0\u0026ndash;7.0) vs 2.0 (0\u0026ndash;11.0), p\u0026thinsp;=\u0026thinsp;0.187] (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTumor size was significantly larger in LVSI positive patients [5.50 cm (2.0\u0026ndash;8.0) vs 3.0 cm (0.30\u0026ndash;9.50), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Hemoglobin value was significantly lower in LVSI positive patients [12.0 g/dL (8.30\u0026ndash;13.70) vs 13.0 g/dL (1.70\u0026ndash;16.70), p\u0026thinsp;=\u0026thinsp;0.002], while platelet count was significantly higher [306.0 x10\u0026sup3;/\u0026micro;L (210.0-530.0) vs 281.0 x10\u0026sup3;/\u0026micro;L (84.0-470.0), p\u0026thinsp;=\u0026thinsp;0.036] (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSerum CA125 level was found to be significantly high in LVSI positive patients [25.90 U/mL (5.60-197.9) vs 15.20 U/mL (4.70-274.80), p\u0026thinsp;=\u0026thinsp;0.030]. No significant difference was found between groups in terms of other laboratory parameters (leukocyte, neutrophil, lymphocyte, monocyte counts and glucose levels) (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic characteristics of the study population according to LVSI status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDemographic Data and Laboratory Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLVSI(-)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLVSI(+)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.06\u0026thinsp;\u0026plusmn;\u0026thinsp;10.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0 (0\u0026ndash;11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0 (0\u0026ndash;7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0 (0\u0026ndash;11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0 (0\u0026ndash;7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0 (0.30\u0026ndash;9.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50 (2.0\u0026ndash;8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeukocytes (x 10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0 (8.10\u0026ndash;16.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0 (8.30\u0026ndash;13.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelets (x 10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281.0 (84.0-470.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306.0 (210.0-530.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil (x 10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0 (0.92\u0026ndash;11.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.15 (2.51\u0026ndash;8.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes(x 10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17 (0.74\u0026ndash;6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22 (0.75\u0026ndash;3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonocytes (x 10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 (0.24\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.25\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.60 (71.30-398.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.0 (87.0-228.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA125\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.20 (4.70-274.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.90 (5.60-197.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.96 (60.09-272.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.25 (88.68\u0026ndash;324.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28 (0.29\u0026ndash;7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54 (1.67\u0026ndash;9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e614.69 (143.11-1984.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e783.93 (507.05-2365.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.90 (6.27-244.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.13 (27.97\u0026ndash;304.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC Analysis and Prediction Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effectiveness of inflammatory markers in predicting LVSI was evaluated with ROC analysis. The highest prediction value was calculated as AUC 0.892 (95% CI: 0.834\u0026ndash;0.950) for SII. AUC values of other markers were found as: PLR 0.647 (95% CI: 0.553\u0026ndash;0.742), NLR 0.655 (95% CI: 0.559\u0026ndash;0.751), GLR 0.608 (95% CI: 0.508\u0026ndash;0.708) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship Between Inflammatory Markers and Clinicopathological Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PLR cut-off value was determined as 130.24 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Clinicopathological features were compared between patients with PLR\u0026thinsp;\u0026ge;\u0026thinsp;130.24 (n\u0026thinsp;=\u0026thinsp;74) and patients with PLR\u0026thinsp;\u0026lt;\u0026thinsp;130.24 (n\u0026thinsp;=\u0026thinsp;82) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In age distribution, the proportion of patients under 55 years was higher in the high PLR group (23 vs 15 patients, p\u0026thinsp;=\u0026thinsp;0.063). There was no significant difference between groups in terms of histological grade (p\u0026thinsp;=\u0026thinsp;0.581). Regarding tumor stage, the rate of Stage IIA and above disease was numerically higher in the high PLR group but did not reach statistical significance (22 vs 16 patients, p\u0026thinsp;=\u0026thinsp;0.138). LVSI positivity was significantly more frequent in patients with PLR\u0026thinsp;\u0026ge;\u0026thinsp;130.24 (13 vs 4 patients, p\u0026thinsp;=\u0026thinsp;0.011). There was no difference between groups in terms of stromal invasion (p\u0026thinsp;=\u0026thinsp;0.228). Lymph node metastasis was significantly more common in the high PLR group (9 vs 3 patients, p\u0026thinsp;=\u0026thinsp;0.043). No difference was found between groups in terms of distant organ metastasis (p\u0026thinsp;=\u0026thinsp;0.599).\u003c/p\u003e\n\u003cp\u003eThe NLR cut-off value was determined as 1.83 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.83 was 109 (69.9%), and the number of patients with NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.83 was 47 (30.1%). Age and histological grade distribution were similar in the high NLR group (p\u0026thinsp;=\u0026thinsp;0.556 and p\u0026thinsp;=\u0026thinsp;0.535, respectively). Regarding tumor stage, the rate of Stage IIA and above disease was higher in patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.83 but did not reach statistical significance (30 vs 8 patients, p\u0026thinsp;=\u0026thinsp;0.161). LVSI positivity was found significantly more frequently in patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.83 (16 vs 1 patient, p\u0026thinsp;=\u0026thinsp;0.021). There was no difference between groups in terms of stromal invasion (p\u0026thinsp;=\u0026thinsp;0.114). No significant difference was found in terms of lymph node metastasis (p\u0026thinsp;=\u0026thinsp;0.687). Distant organ metastasis was significantly more common in the high NLR group (7 vs 0 patients, p\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e\n\u003cp\u003eThe SII cut-off value was determined as 506.38 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of patients with SII\u0026thinsp;\u0026ge;\u0026thinsp;506.38 was 108 (69.2%), and the number of patients with SII\u0026thinsp;\u0026lt;\u0026thinsp;506.38 was 48 (30.8%). Histological grade distribution was significantly different in the high SII group; Grade 1 tumor rate was higher in the low SII group, while Grade 2\u0026ndash;3 tumor rate was higher in the high SII group (p\u0026thinsp;=\u0026thinsp;0.045). LVSI positivity was significantly more common in patients with SII\u0026thinsp;\u0026ge;\u0026thinsp;506.38 (17 vs 0 patients, p\u0026thinsp;=\u0026thinsp;0.004). There was no difference between groups in terms of stromal invasion (p\u0026thinsp;=\u0026thinsp;0.264). Lymph node metastasis was numerically more common in the high SII group but did not reach statistical significance (11 vs 1 patient, p\u0026thinsp;=\u0026thinsp;0.080). Distant organ metastasis was found significantly more frequently in the high SII group (7 vs 0 patients, p\u0026thinsp;=\u0026thinsp;0.021).\u003c/p\u003e\n\u003cp\u003eThe GLR cut-off value was determined as 44.61 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of patients with GLR\u0026thinsp;\u0026ge;\u0026thinsp;44.61 was 99 (63.5%), and the number of patients with GLR\u0026thinsp;\u0026lt;\u0026thinsp;44.61 was 57 (36.5%). The proportion of patients with tumor size\u0026thinsp;\u0026ge;\u0026thinsp;2 cm was significantly higher in the high GLR group (74 vs 51 patients, p\u0026thinsp;=\u0026thinsp;0.026). There was no significant difference between groups in terms of histological grade and tumor stage (p\u0026thinsp;=\u0026thinsp;0.866 and p\u0026thinsp;=\u0026thinsp;0.058, respectively). LVSI positivity was significantly more frequent in patients with GLR\u0026thinsp;\u0026ge;\u0026thinsp;44.61 (15 vs 2 patients, p\u0026thinsp;=\u0026thinsp;0.025). Stromal invasion was also significantly more common in this group (17 vs 3 patients, p\u0026thinsp;=\u0026thinsp;0.032). No significant difference was found between groups in terms of lymph node metastasis and distant organ metastasis (p\u0026thinsp;=\u0026thinsp;0.810 and p\u0026thinsp;=\u0026thinsp;0.211, respectively).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship between preoperative inflammatory markers and clinicopathological features\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;130.24\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;130.24\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.83\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1.83\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;506.38\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;506.38\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;44.61\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;44.61\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistologic grade, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IA1,IA2,IA3,IB,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IIA and higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVSI, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStromal invasion, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNM, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDistant Organ Metastases, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of Prediction Model for LVSI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn univariate logistic regression analysis, factors associated with LVSI were identified as: PLR\u0026thinsp;\u0026ge;\u0026thinsp;130.24 (p\u0026thinsp;=\u0026thinsp;0.011), NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.83 (p\u0026thinsp;=\u0026thinsp;0.021), SII\u0026thinsp;\u0026ge;\u0026thinsp;506.38 (p\u0026thinsp;=\u0026thinsp;0.004), and GLR\u0026thinsp;\u0026ge;\u0026thinsp;44.61 (p\u0026thinsp;=\u0026thinsp;0.025). In multivariate logistic regression analysis performed to predict LVSI presence, high PLR (OR: 3.70, 95% CI: 1.11\u0026ndash;12.31, p\u0026thinsp;=\u0026thinsp;0.033), high NLR (OR: 8.36, 95% CI: 1.06\u0026ndash;65.88, p\u0026thinsp;=\u0026thinsp;0.044), and high GLR (OR: 4.49, 95% CI: 0.97\u0026ndash;20.79, p\u0026thinsp;=\u0026thinsp;0.054) were found to increase LVSI risk (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate logistic regression analysis results for the presence of Lymphovascular Space Invasion (LVSI)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% Confidence Interval (CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLR\u0026thinsp;\u0026ge;\u0026thinsp;130.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u0026ndash;12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026ndash;65.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLR\u0026thinsp;\u0026ge;\u0026thinsp;44.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u0026ndash;20.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eA prediction nomogram based on these three independent risk factors was created (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe nomogram presented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e is designed for practical clinical use to estimate the preoperative risk of LVSI. To utilize the nomogram, a clinician first determines a patient\u0026apos;s status for each of the three variables: PLR, NLR, and GLR, based on the established cut-off values (PLR\u0026thinsp;\u0026ge;\u0026thinsp;130.24, NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.83, and GLR\u0026thinsp;\u0026ge;\u0026thinsp;44.61, respectively). For each variable, the corresponding point value is identified by drawing a vertical line from the patient\u0026rsquo;s category on the variable\u0026apos;s axis up to the \u0026apos;Points\u0026apos; axis at the top. The points for all three variables are then summed to obtain a \u0026apos;Total Points\u0026apos; score. Finally, this total score is located on the \u0026apos;Total Points\u0026apos; axis, and another vertical line is projected downwards to the \u0026apos;Risk of LVSI\u0026apos; axis to determine the final predicted probability of LVSI for that patient.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt has been reported that preoperative inflammation markers are associated with clinical features in endometrioid-type EC before; however, the role of novel GLR has not been evaluated before. In this study it was shown that preoperative inflammatory markers; GLR, PLR, and NLR are associated with LVSI in endometrioid EC. In addition, a new, practical, 3-variable nomogram from these markers was developed for preoperative prediction of LVSI for the first time.\u003c/p\u003e\u003cp\u003eGLR has emerged as a novel biomarker whose prognostic significance has been investigated in various solid tumors in recent years. In our study, high GLR values were found to be significantly associated with LVSI in univariate analysis (p\u0026thinsp;=\u0026thinsp;0.025). However, in the multivariate model containing other strong inflammatory markers such as PLR and NLR, the role of GLR as an independent predictive factor remained at the border of statistical significance (p\u0026thinsp;=\u0026thinsp;0.054). This fundamental relationship between GLR and LVSI, an aggressive tumor characteristic, aligns with findings from studies emphasizing its prognostic importance in other solid tumors such as pancreatic, lung, and breast cancer (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Studies in gastric, pancreatic and lung cancer patients have shown that high GLR values are associated with aggressive tumor characteristics and poor overall survival (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe prognostic value of GLR stems from its composite structure based on the combined evaluation of glucose metabolism and systemic immune status. A particularly noteworthy finding is that while glucose (p\u0026thinsp;=\u0026thinsp;0.096) and lymphocyte (p\u0026thinsp;=\u0026thinsp;0.566) levels alone showed no significant relationship with LVSI presence in our study, GLR, which is the ratio of these two values, emerged as a significant marker (p\u0026thinsp;=\u0026thinsp;0.025). This outcome underscores the utility of composite markers, which can reveal prognostic relationships that are not apparent when their individual components are analyzed alone. While high glucose levels increase tumor cell growth and invasive capacity, low lymphocyte counts indicate suppression of immune response, and the combination of these two factors is associated with aggressive tumor behaviour (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe prognostic role of GLR in endometrioid EC can be explained by the effect of hyperglycemia on the tumor microenvironment and lymphocytopenia's suppression of anti-tumor immune response. This is also supported by the known relationship between glucose metabolism and EC risk. The meta-analysis study by Galeone et al. showed that high glycemic load increases EC risk (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This suggests that hyperglycemia may promote endometrial carcinogenesis through insulin and insulin-like growth factors. In our study, the finding that GLR is associated with aggressive tumor characteristics such as LVSI, deep myometrial invasion, and lymph node metastasis indicates that glucose metabolism disorder may be effective not only in cancer development but also in aggressive tumor behavior.\u003c/p\u003e\u003cp\u003eIn our study, significantly more aggressive clinicopathological features were detected in patients with PLR\u0026thinsp;\u0026ge;\u0026thinsp;130.24 compared to patients with low PLR values. Our finding in EC is consistent with the existing literature. In the study by Cummings et al. including 605 EC patients, high PLR values were found to show significant correlation with advanced FIGO stage, presence of LVSI, and lymph node positivity (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Similarly, another study conducted in non-endometrioid endometrial cancers showed that PLR is associated with disease aggressiveness (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These findings suggest that systemic inflammatory markers have prognostic value in different histological subtypes of EC.\u003c/p\u003e\u003cp\u003eThe comprehensive meta-analysis by Ni et al. including 9 studies and 3390 EC provides strong evidence for the prognostic value of PLR in EC (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This meta-analysis showed the negative effect of high PLR values on both overall survival (pHR\u0026thinsp;=\u0026thinsp;1.99, 95% CI 1.51\u0026ndash;2.61) and disease-free survival (pHR\u0026thinsp;=\u0026thinsp;2.02, 95% CI 1.45\u0026ndash;2.80). More importantly, subgroup analyses found that the prognostic value of PLR was consistently maintained at different cut-off values (\u0026le;\u0026thinsp;190.78 and \u0026gt;\u0026thinsp;190.78) and that this relationship was valid in different analysis methods (univariate and multivariate). These findings strongly support that PLR is a reliable and reproducible prognostic marker in EC.\u003c/p\u003e\u003cp\u003eOur study showed that high NLR values are significantly associated with LVSI positivity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This finding supports that systemic inflammation is closely related to tumor aggressiveness in EC. The relationship of NLR with LVSI emerges as an important prognostic indicator in EC literature. In a study on non-endometrioid EC, LVSI positivity was found to be significantly higher in the high NLR group Muangto et al. also showed that NLR is statistically significantly associated with myometrial invasion depth in EC, reporting that NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.93 predicts more than half myometrial invasion with 83.3% sensitivity and 52.8% specificity (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eParticularly noteworthy is that NLR shows different cut-off values in different cancer types. While values above 3 are generally recommended in meta-analyses for solid tumors, lower values such as 1.93 can also be found significant in EC (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This situation reflects cancer type-specific biological differences and variability in the inflammatory profile of the tumor microenvironment.\u003c/p\u003e\u003cp\u003eThe underlying mechanisms of NLR's relationship with lymphovascular invasion are complex and multifaceted. As stated in the review by Mosca et al., high NLR values were found to be associated with increased peritumoral macrophage infiltration and high levels of various pro-inflammatory cytokines including IL-1ra, IL-6, IL-7, IL-8, IL-12, IL-17, MCP-1, and PDGF-BB. This inflammatory microenvironment facilitates LVSI development by increasing the invasion capacity of tumor cells into vascular and lymphatic structures. Neutrophils can exhibit two different phenotypes in the tumor microenvironment according to their polarization: N1 (anti-tumor) and N2 (pro-tumor) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). N2 neutrophils support the tumor invasion process through neo-angiogenesis induction, stroma remodeling, and extracellular matrix degradation, while also inhibiting T cell proliferation. Particularly, neutrophil-derived enzymes such as neutrophil elastase (NE) and metalloproteinase 9 (MMP9) increase the invasion ability of tumor cells into vascular structures by providing degradation of the extracellular matrix.\u003c/p\u003e\u003cp\u003eIn our study, the SII cut-off value was determined as 506.38, and patients with SII\u0026thinsp;\u0026ge;\u0026thinsp;506.38 were found to show aggressive clinicopathological features. The high SII group was characterized by significantly higher histological grade (Grade 2\u0026ndash;3 rate 32.4% vs 14.6%, p\u0026thinsp;=\u0026thinsp;0.045), LVSI positivity (17 vs 0 patients, p\u0026thinsp;=\u0026thinsp;0.004), and distant organ metastasis (7 vs 0 patients, p\u0026thinsp;=\u0026thinsp;0.021). Although SII demonstrated the strongest univariate association with LVSI, it was excluded from the final multivariate model due to the statistical instability (quasi-complete separation) it introduced. This may be due to all 17 LVSI positive patients in our study also being in the high SII group and creating near-perfect separation in the model. This situation may particularly stem from the imbalance between LVSI positive and negative groups in the sample.\u003c/p\u003e\u003cp\u003eIn the comprehensive series by Matsubara et al. including 442 patients, the SII cut-off value was determined as 931, and the high SII group constituted 30.5% of patients (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In this study, high SII showed strong correlation with advanced FIGO stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), non-endometrioid histology (p\u0026thinsp;=\u0026thinsp;0.029), high tumor grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LVSI positivity (p\u0026thinsp;=\u0026thinsp;0.001), and positive peritoneal cytology (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, in our study, LVSI positivity was found significantly more frequently in patients with SII\u0026thinsp;\u0026ge;\u0026thinsp;506.38 (17 vs 0 patients, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003cp\u003eA prospective study including 522 EC patients examined in detail the relationship between pre-treatment inflammatory markers and clinicopathological features (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This study showed that NLR and SII are associated with adverse clinicopathological factors. Particularly noteworthy is that the prognostic value of inflammatory markers was preserved despite 67.2% of patients having low grade and 85.4% having early stage disease.\u003c/p\u003e\u003cp\u003eJi and Wang's systematic meta-analysis comprehensively evaluated the prognostic importance of SII in gynecological and breast cancers (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This meta-analysis including 9 articles and 2724 patients proved that high SII is associated with poor overall survival (HR\u0026thinsp;=\u0026thinsp;2.12, 95% CI, 1.61\u0026ndash;2.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), disease-free survival (HR\u0026thinsp;=\u0026thinsp;2.28, 95% CI 1.52\u0026ndash;3.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and increased lymph node metastasis risk (RR\u0026thinsp;=\u0026thinsp;1.34, 95% CI 1.20\u0026ndash;1.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analyses indicated that the prognostic value of SII was particularly prominent in ovarian cancer and triple-negative breast cancer, but limited data were available for EC.\u003c/p\u003e\u003cp\u003eThe study by Yang et al. in pancreatic cancer constitutes an important example for the development of multifactorial scoring systems including SII (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The PIIN (prognostic immune-inflammatory-nutritional) score developed in this study combines NLR, SII, fibrinogen, ALBI score, and PNI parameters. A significant relationship was found with tumor localization (p\u0026thinsp;=\u0026thinsp;0.003) and postoperative complications (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in patients with PIIN score\u0026thinsp;\u0026ge;\u0026thinsp;37.2, and it was proven to be an independent prognostic factor in multivariate analysis (HR\u0026thinsp;=\u0026thinsp;2.171, 95% CI\u0026thinsp;=\u0026thinsp;1.207\u0026ndash;3.906, p\u0026thinsp;=\u0026thinsp;0.010). This approach inspires the development of similar combined scoring systems in EC.\u003c/p\u003e\u003cp\u003eThe present study addresses the literature gap on the role of GLR in endometrioid-type EC, a promising biomarker in other solid tumors. Our research successfully demonstrates that preoperative GLR, alongside PLR and NLR, are independent predictive factors for LVSI. The key clinical output of this work is a practical nomogram based on these three accessible and low-cost blood parameters. This novel tool has the potential to be a valuable aid for clinicians, offering a non-invasive method to predict LVSI risk, thereby helping to identify high-risk patients and personalize surgical strategies, such as the decision for lymphadenectomy.\u003c/p\u003e\u003cp\u003eThe methodological strengths of this study include its homogeneous patient population (consisting of only endometrioid-type EC), the use of standardized histopathological evaluation criteria, and the determination of optimal cut-off values via ROC analysis. However, the study has several limitations. The retrospective, single-center design carries an inherent risk of selection bias and limits the generalizability of our findings. Furthermore, LVSI assessment is a subjective parameter with potential for inter-observer variability. While the patient cohort (n\u0026thinsp;=\u0026thinsp;156) was sufficient for primary statistical analyses, it was relatively small for detailed subgroup analyses. Other key limitations include the inability to perform survival analysis due to a lack of long-term follow-up data, the potential confounding effects of comorbidities which were not evaluated in detail, and the absence of molecular subtyping (POLE, MSI, p53), as this analysis was not available for the retrospective cohort.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study establishes that preoperative inflammatory markers\u0026mdash;PLR, NLR, and GLR are clinically valuable tools for predicting LVSI in endometrioid EC. We have developed a practical, 3-variable nomogram from these markers, offering a promising decision-support tool for preoperative risk stratification and the personalization of surgical management. Our findings particularly highlight the need for more detailed investigation into GLR's prognostic role. Future research, including large-scale prospective and multicenter studies, is essential to validate our model's clinical effectiveness. Combining these inflammatory markers with molecular subtyping and investigating their impact on treatment response in longitudinal studies will be critical steps toward advancing personalized medicine in endometrial cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eALBI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlbumin-Bilirubin Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBSO\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBilateral Salpingo-Oophorectomy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCA125\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCancer Antigen 125\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCOVID-19\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCoronavirus Disease 2019\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEndometrial Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eFIGO\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Federation of Gynecology and Obstetrics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlucose to Lymphocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLVSI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLymphovascular Space Invasion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCP-1\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMonocyte Chemoattractant Protein-1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMELF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMicrocystic, Elongated and Fragmented\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMMP9\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMatrix Metalloproteinase 9\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMSI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMicrosatellite Instability\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil Elastase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil to Lymphocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePDGF-BB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet-Derived Growth Factor-BB\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePIIN\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrognostic Immune-Inflammatory-Nutritional\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet to Lymphocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrognostic Nutritional Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePOLE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDNA Polymerase Epsilon\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRelative Risk\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic Immune-Inflammation Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTAH\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal Abdominal Hysterectomy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e The study was conducted in accordance with the guidelines of the Helsinki Declaration. Written informed consent was obtained from all participants. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (with the ethics committee decision numbered 2024/52 dated 03.06.2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e KA, GU and HY developed the concept and were responsible for data collection. TTI and KA planned the study. KA, TTI and ZCK analysed the results. KA, SGG and HA wrote the manuscript text and prepared figures and tables. All authors reviewed the manuscript for important intellectual content and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi J, Zhao X, Li X, Lin K, Zeng Z, Ning Z, et al. Analysis of the disease burden of malignancies in the female reproductive system in China from 1990 to 2019: an age-period-cohort study and joinpoint analysis. BMJ Open. 2025;15(4):e081511.\u003c/li\u003e\n\u003cli\u003eXie J, Maguire FB, Hofer BM, Cooley JJP, Chen HA, Parikh-Patel A, et al. Disparities in hysterectomy-corrected endometrial cancer incidence trends by histologic subtype among racial/ethnic groups in California, 2012-2019. Gynecol Oncol. 2025;197:34-42.\u003c/li\u003e\n\u003cli\u003ePan X, Li J, Liu P, Li J, Zhao M, Wu Y, et al. Global trends in endometrial cancer and metabolic syndrome research: A bibliometric and visualization analysis. Comput Biol Med. 2025;192(Pt B):110362.\u003c/li\u003e\n\u003cli\u003eHabeshian TS, Park SY, Conti D, Wilkens LR, Marchand LL, Setiawan VW. Inflammatory and insulinemic dietary and lifestyle patterns and incidence of endometrial cancer: the multiethnic cohort. Am J Clin Nutr. 2025;121(6):1236-45.\u003c/li\u003e\n\u003cli\u003eNjoku K, Ramchander NC, Wan YL, Barr CE, Crosbie EJ. Pre-treatment inflammatory parameters predict survival from endometrial cancer: A prospective database analysis. Gynecol Oncol. 2022;164(1):146-53.\u003c/li\u003e\n\u003cli\u003eMarin AG, Filipescu AG, Petca RC, Vlădăreanu R, Petca A. Clinical Correlations between Serological Markers and Endometrial Cancer. Cancers (Basel). 2024;16(10).\u003c/li\u003e\n\u003cli\u003eMa L, Zhang Y, Shao Y, Luo L, Zhou J, Wu J, et al. Prognostic significance of systemic inflammatory response markers NLR, PLR, and MLR in advanced high-risk endometrial cancer following radiotherapy. Am J Cancer Res. 2025;15(3):966-75.\u003c/li\u003e\n\u003cli\u003eLin H, Zhong W, Zhong L, Que C, Lin X. The inflammatory markers combined with CA125 may predict postoperative survival in endometrial cancer. J Obstet Gynaecol. 2024;44(1):2373937.\u003c/li\u003e\n\u003cli\u003eAhn JH, Lee SJ, Yoon JH, Park DC, Kim SI. Prognostic value of pretreatment systemic inflammatory markers in patients with stage I endometrial cancer. Int J Med Sci. 2022;19(14):1989-94.\u003c/li\u003e\n\u003cli\u003eLiu R, Shen Y, Cui J, Ma W, Wang J, Chen C, et al. Association between glucose to lymphocyte ratio and prognosis in patients with solid tumors. Front Immunol. 2024;15:1454393.\u003c/li\u003e\n\u003cli\u003ePark SH, Kang IC, Hong SS, Kim HY, Hwang HK, Kang CM. Glucose-to-Lymphocyte Ratio (GLR) as an Independent Prognostic Factor in Patients with Resected Pancreatic Ductal Adenocarcinoma-Cohort Study. Cancers (Basel). 2024;16(10).\u003c/li\u003e\n\u003cli\u003ePalmieri E, Mariani A, Coleman R, Croce S, Hui P, Lax S, et al. The new 2023 endometrial cancer FIGO staging system: balancing innovation with complexity. Int J Gynecol Cancer. 2025:101823.\u003c/li\u003e\n\u003cli\u003eZhao X, Sun F, Leng N, Zhang X, Zhu Y. The past, present, and future of FIGO staging of endometrial cancer. J Gynecol Oncol. 2025.\u003c/li\u003e\n\u003cli\u003eSong ZX, Leng H, Zhao XY, Zhu L, Liu Y, Liu CR. [Predictive significance of microcystic elongated and fragmented (MELF) growth pattern in the prognosis of no specific molecular profile endometrial endometrioid carcinoma]. Zhonghua Yi Xue Za Zhi. 2025;105(10):745-52.\u003c/li\u003e\n\u003cli\u003eDubey A, Ahuja S, Zaheer S. Prognostic significance of tumor budding in endometrial cancer: clinicopathological insights. Korean J Clin Oncol. 2025;21(1):9-12.\u003c/li\u003e\n\u003cli\u003eYang M, Zhang Q, Ge YZ, Tang M, Hu CL, Wang ZW, et al. Prognostic Roles of Glucose to Lymphocyte Ratio and Modified Glasgow Prognosis Score in Patients With Non-small Cell Lung Cancer. Front Nutr. 2022;9:871301.\u003c/li\u003e\n\u003cli\u003eYıldırım MB, \u0026Ouml;zkan MB. Prognostic value of preoperative glucose to lymphocytes ratio in patients with resected gastric cancer. Journal of Surgery and Medicine. 2021;5(9):889-93.\u003c/li\u003e\n\u003cli\u003eZhong A, Cheng CS, Kai J, Lu R, Guo L. Clinical Significance of Glucose to Lymphocyte Ratio (GLR) as a Prognostic Marker for Patients With Pancreatic Cancer. Front Oncol. 2020;10:520330.\u003c/li\u003e\n\u003cli\u003eGaleone C, Augustin LS, Filomeno M, Malerba S, Zucchetto A, Pelucchi C, et al. Dietary glycemic index, glycemic load, and the risk of endometrial cancer: a case-control study and meta-analysis. Eur J Cancer Prev. 2013;22(1):38-45.\u003c/li\u003e\n\u003cli\u003eCummings M, Merone L, Keeble C, Burland L, Grzelinski M, Sutton K, et al. Preoperative neutrophil:lymphocyte and platelet:lymphocyte ratios predict endometrial cancer survival. Br J Cancer. 2015;113(2):311-20.\u003c/li\u003e\n\u003cli\u003eSong H, Jeong MJ, Cha J, Lee JS, Yoo JG, Song MJ, et al. Preoperative neutrophil-to-lymphocyte, platelet-to-lymphocyte and monocyte-to-lymphocyte ratio as a prognostic factor in non-endometrioid endometrial cancer. Int J Med Sci. 2021;18(16):3712-7.\u003c/li\u003e\n\u003cli\u003eNi L, Tao J, Xu J, Yuan X, Long Y, Yu N, et al. Prognostic values of pretreatment neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios in endometrial cancer: a systematic review and meta-analysis. Arch Gynecol Obstet. 2020;301(1):251-61.\u003c/li\u003e\n\u003cli\u003eMuangto T, Maireang K, Poomtavorn Y, Thaweekul Y, Punyashthira A, Chantawong N, et al. Study on Preoperative Neutrophil/Lymphocyte (NLR) and Platelet/Lymphocyte Ratio (PLR) as a Predictive Factor in Endometrial Cancer. Asian Pac J Cancer Prev. 2022;23(10):3317-22.\u003c/li\u003e\n\u003cli\u003eMosca M, Nigro MC, Pagani R, De Giglio A, Di Federico A. Neutrophil-to-Lymphocyte Ratio (NLR) in NSCLC, Gastrointestinal, and Other Solid Tumors: Immunotherapy and Beyond. Biomolecules. 2023;13(12).\u003c/li\u003e\n\u003cli\u003eMatsubara S, Mabuchi S, Takeda Y, Kawahara N, Kobayashi H. Prognostic value of pre-treatment systemic immune-inflammation index in patients with endometrial cancer. PLoS One. 2021;16(5):e0248871.\u003c/li\u003e\n\u003cli\u003eJi Y, Wang H. Prognostic prediction of systemic immune-inflammation index for patients with gynecological and breast cancers: a meta-analysis. World J Surg Oncol. 2020;18(1):197.\u003c/li\u003e\n\u003cli\u003eYang J, Zhou H, Li H, Zhao F, Tong K. Nomogram incorporating prognostic immune-inflammatory-nutritional score for survival prediction in pancreatic cancer: a retrospective study. BMC Cancer. 2024;24(1):193.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glucose to lymphocyte ratio, endometrial cancer, lymphovascular space invasion, nomogram, prognostic biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7054950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7054950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe role of systemic inflammation in cancer prognosis has garnered increasing interest. The prognostic significance of novel inflammatory markers such as glucose to lymphocyte ratio (GLR) in endometrioid-type endometrial cancer (EC) and their relationship with clinicopathological parameters have not yet been fully elucidated. This study aimed to evaluate the association of preoperative inflammatory markers, particularly with lymphovascular space invasion (LVSI), and aggressive tumor characteristics, and to develop a model predicting LVSI presence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from 156 patients who underwent surgical treatment for endometrioid-type EC were retrospectively analysed. Optimal threshold values for inflammatory markers such as GLR, platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), and Systemic Immune-Inflammation Index (SII) calculated from preoperative blood tests in predicting LVSI were determined using ROC analysis. Multivariate logistic regression analysis was used to identify independent risk factors, and a nomogram predicting LVSI was created.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigh PLR (OR: 3.70, p\u0026thinsp;=\u0026thinsp;0.033) and high NLR (OR: 8.36, p\u0026thinsp;=\u0026thinsp;0.044) values were identified as independent risk factors for LVSI. High GLR showed a strong trend towards an increased LVSI risk (OR: 4.49, p\u0026thinsp;=\u0026thinsp;0.054). SII, which showed the strongest association with LVSI in univariate analysis (p\u0026thinsp;=\u0026thinsp;0.004), could not be included in multivariate analysis due to statistical instability in the model. A nomogram based on PLR, NLR, and GLR was successfully developed to predict LVSI.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePreoperative inflammatory markers are valuable, low-cost, and easily accessible tools for predicting LVSI presence in endometrioid-type EC. The developed nomogram may assist clinicians in identifying high-risk patients and personalizing surgical strategy.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003eRetrospectively registered. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (No: 2024/52, Date: 03.06.2024).\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of Inflammatory Marker Combinations for Clinicopathological Features in Endometrial Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 05:34:03","doi":"10.21203/rs.3.rs-7054950/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":"bdf88b89-7bfd-4b24-8791-93498aaa87b8","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T08:27:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 05:34:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7054950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7054950","identity":"rs-7054950","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}