Prognostic Value of Systemic Inflammatory Markers in Patients with Endometrioid-Type Endometrial Cancer: A Retrospective Analysis

Prognostic Value of Systemic Inflammatory Markers in Patients with Endometrioid-Type Endometrial Cancer: A Retrospective Analysis | 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 Value of Systemic Inflammatory Markers in Patients with Endometrioid-Type Endometrial Cancer: A Retrospective Analysis Gorkem ULGER, Kasim AKAY, Hamza YILDIZ, Pelin AYTAN, Sevki Goksun GOKULU, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8414305/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Systemic inflammatory markers have emerged as prognostic indicators in various malignancies. This study investigates the relationship between preoperative inflammatory markers and survival outcomes in patients with endometrioid-type endometrial cancer. Methods This retrospective study analyzed 142 patients with endometrioid-type endometrial cancer who underwent surgical treatment between 2018 and 2024. Patients with coexistent adenomyosis and myoma uteri were excluded to eliminate confounding inflammatory effects. Preoperative inflammatory markers including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), pan-immune-inflammation value (PIV), and other hematological parameters were analyzed. Results The median follow-up period was 28.0 months (range: 1–51 months, IQR: 16–37 months). Overall survival rate was 44.344 ± 1.278 months. Cumulative survival rates were 96% at 12 months, 88% at 24 months, 82% at 36 months, and 72% at 48 months. Several inflammatory markers showed significant prognostic value: DNI ≥ 0.005763 (HR: 2.485, 95% CI: 1.011–5.644), SIRI ≥ 2.059 (HR: 2.485, 95% CI: 1.042–5.923), and GLR ≥ 58.88 (HR: 2.841, 95% CI: 1.206–6.693). Conclusions Preoperative inflammatory markers, particularly SIRI and GLR, demonstrate significant prognostic value in endometrioid-type endometrial cancer. These readily available biomarkers may enhance risk stratification and guide personalized treatment strategies. endometrial cancer inflammatory markers prognosis neutrophil-lymphocyte ratio systemic inflammatory response index Introduction Endometrial cancer is the most common gynecologic malignancy in developed countries, with endometrioid-type adenocarcinoma representing approximately 80% of all cases [ 1 ]. Despite generally favorable outcomes for early-stage disease, accurate prognostic assessment remains crucial for treatment planning and patient counseling. Traditional prognostic factors include histologic grade, depth of myometrial invasion, lymphovascular space invasion, and lymph node status [ 2 ]. The tumor microenvironment and host immune response play critical roles in cancer progression and patient outcomes. Systemic inflammatory markers derived from routine blood tests have emerged as cost-effective prognostic tools across various malignancies [ 3 , 4 ]. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) have been extensively studied as markers of systemic inflammation and immune dysfunction [ 5 ]. Novel inflammatory indices such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation value (PIV) have been proposed as potentially superior prognostic markers [ 6 ]. The SIRI, calculated as neutrophil count multiplied by monocyte count divided by lymphocyte count, incorporates three key immune cell populations and may provide a more comprehensive assessment of systemic inflammation than traditional ratios [ 7 ]. However, their utility in endometrial cancer, particularly in the endometrioid subtype, remains understudied. This study aims to evaluate the prognostic value of comprehensive inflammatory markers in a carefully selected cohort of patients with endometrioid-type endometrial cancer, excluding those with adenomyosis and myoma uteri to minimize inflammatory confounders and provide more accurate assessment of cancer-related inflammatory status. Materials and Methods This retrospective study was conducted at the Gynecological Oncology Clinic, Department of Obstetrics and Gynecology, Mersin University Faculty of Medicine Hospital. The study protocol was approved by the Mersin University Clinical Research Ethics Committee (Decision No: 2024/532, June 5, 2024) and conducted in accordance with the Declaration of Helsinki. From a total of 195 patients who underwent surgery for endometrioid-type endometrial cancer between 2018 and 2024, 142 patients met the inclusion criteria and were included in the final analysis. The patient selection process involved careful screening to exclude confounding factors that could influence inflammatory marker interpretation. The inclusion criteria comprised patients aged 18 years or older with histologically confirmed endometrioid-type endometrial cancer who underwent complete surgical staging with available preoperative blood count data within 7 days before surgery and complete follow-up data. The exclusion criteria were designed to eliminate potential confounders of inflammatory markers and included non-endometrioid histological subtypes, previous surgery for endometrial cancer, prior chemotherapy or radiotherapy, coexistent adenomyosis confirmed on final histopathology, coexistent uterine myomas (leiomyomas) confirmed on final histopathology, active inflammatory conditions or infections, hematological disorders, insufficient medical records, and age less than 18 years. The patient selection process began with 195 endometrioid-type endometrial cancer patients who underwent surgery during the study period. Following systematic application of exclusion criteria, patients with adenomyosis were excluded based on final histopathological examination showing ectopic endometrial glands and stroma invading the myometrium at least 2.5 mm beyond the endometrial-myometrial junction. Patients with myoma uteri were excluded when final histopathology confirmed the presence of leiomyomas. Additional exclusions included patients with incomplete data, those with other exclusion criteria, resulting in the final study cohort of 142 patients. Patient demographics, clinical characteristics, surgical details, and histopathological findings were retrospectively collected from medical records and the electronic patient registration system. All histopathological specimens were reviewed by experienced gynecologic pathologists at our institution using standardized criteria. Tumor staging was performed according to the FIGO 2023 system, and tumor grading followed WHO classification guidelines. Preoperative blood samples were obtained within 7 days before surgery after overnight fasting and analyzed using automated hematology analyzers. Complete blood count parameters including white blood cell count, neutrophil count, lymphocyte count, monocyte count, platelet count, and hemoglobin levels were recorded. Additional laboratory parameters including glucose levels, liver function tests, and when available, C-reactive protein and tumor markers were documented. Various inflammatory markers were calculated using established formulas. The neutrophil-to-lymphocyte ratio (NLR) was calculated as neutrophil count divided by lymphocyte count. The platelet-to-lymphocyte ratio (PLR) was calculated as platelet count divided by lymphocyte count. The monocyte-to-lymphocyte ratio (MLR) was calculated as monocyte count divided by lymphocyte count. The systemic immune-inflammation index (SII) was calculated as neutrophil count multiplied by platelet count divided by lymphocyte count. The systemic inflammatory response index (SIRI) was calculated as neutrophil count multiplied by monocyte count divided by lymphocyte count. The pan-immune-inflammation value (PIV) was calculated as neutrophil count multiplied by platelet count multiplied by monocyte count divided by lymphocyte count. The lymphocyte-to-monocyte ratio (LMR) was calculated as lymphocyte count divided by monocyte count. The glucose-to-lymphocyte ratio (GLR) was calculated as glucose level divided by lymphocyte count. All patients underwent comprehensive surgical staging according to established guidelines. The surgical approach included total hysterectomy and bilateral salpingo-oophorectomy. Lymph node dissection decisions were based on intraoperative frozen section analysis and established criteria. Pelvic lymphadenectomy was performed for Grade 1–2 tumors with diameter greater than 2 cm, myometrial invasion greater than 50%, or cervical stromal involvement. Para-aortic lymphadenectomy was performed for Grade 3 tumors regardless of other factors, and for selected Grade 1–2 cases based on intraoperative findings. The extent of lymph node dissection was determined by the surgical team based on individual patient characteristics and institutional protocols. Statistical analysis was performed using SPSS 22.0 software package. Continuous variables were expressed as mean ± standard deviation for normally distributed data or median with minimum and maximum values for non-normally distributed data. Categorical variables were presented as frequencies and percentages. The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. Optimal cut-off values for inflammatory markers were determined using receiver operating characteristic (ROC) curve analysis, with the Youden index (sensitivity + specificity − 1) used to identify the most discriminating threshold. Survival analysis was conducted using the Kaplan-Meier method with log-rank test for comparisons between groups. Univariate and multivariate Cox proportional hazards regression models were used to identify independent prognostic factors for overall survival. Variables with p < 0.10 in univariate analysis were included in multivariate models. Statistical significance was set at p < 0.05 for all analyses. Results A total of 142 patients with endometrioid-type endometrial cancer were included in the analysis. The mean age was 59.75 ± 9.01 years. Patient demographics showed that 95 patients (66.9%) were younger than 65 years, while 47 patients (33.1%) were 65 years or older. The median gravidity was 2.0 (range: 0–11), and median parity was 2.0 (range: 0–11). Regarding surgical approach, 93 patients (65.5%) underwent laparotomy while 49 patients (34.5%) had laparoscopic surgery. Lymph node dissection patterns varied based on risk factors, with 26 patients (18.3%) not undergoing lymph node dissection, 53 patients (37.3%) having only pelvic lymphadenectomy, and 63 patients (44.4%) receiving both pelvic and para-aortic lymphadenectomy. Comorbidity analysis revealed that 53 patients (37.3%) had diabetes mellitus, 65 patients (45.8%) had hypertension, 34 patients (23.9%) had both diabetes and hypertension, 13 patients (9.2%) had coronary artery disease, and 59 patients (41.5%) had other additional diseases. These comorbidities were considered in the analysis as potential confounders of inflammatory marker interpretation. Histopathological examination showed that the majority of tumors were well-differentiated, with 104 patients (73.2%) having Grade 1 tumors, 26 patients (18.3%) having Grade 2 tumors, and 12 patients (8.5%) having Grade 3 tumors. Tumor size analysis revealed that 27 patients (19%) had tumors smaller than 2 cm, while 115 patients (81%) had tumors 2 cm or larger. Myometrial invasion assessment showed that 95 patients (66.9%) had invasion less than 50% of myometrial thickness, while 47 patients (33.1%) had invasion of 50% or greater. Lymphovascular space invasion (LVSI) was present in 19 patients (13.4%) and stromal invasion was detected in 20 patients (14.1%). Assessment of extrauterine spread showed that pelvic involvement was found in 12 patients (8.5%), para-aortic involvement in 4 patients (2.8%), serosa and adnexal involvement in 4 patients (2.8%), vaginal-parametrial involvement in 2 patients (1.4%), and distant metastasis in 6 patients (4.2%). The majority of patients (130 patients, 91.5%) had no pelvic involvement, and 138 patients (97.2%) had no para-aortic involvement. Table 1 : Patient Demographics, Clinical Characteristics, and Survival Analysis Table 1 Patient Demographics, Clinical Characteristics, and Survival Analysis Variable Category Mean ± SD/ Median(min-max) Total Alive Status p HR (95% CI) Alive Ex Age (Year) 59,75 ± 9,01 142 (100%) 120 22 0,020 1.06 (1,01–1,11) Age (Year) < 65 54,98 ± 5,84 95 (66,9%) 86(90,6%) 9(9,4%) 0,017 2,70 (1,15 − 6,33) ≥ 65 69,77 ± 4,44 47 (33,1%) 34(72,3%) 13(27,7%) Gravity 2,0 (0–11) 142 (100%) 0,334 Parity 2,0 (0–11) 142 (100%) 0,441 Operation Laparotomy 93 (65,5%) 74(79,6%) 19(20,4%) 0,087 Laparoscopy 49 (34,5%) 46(93,9%) 3(6,1%) LND Not Done 26 (18,3%) 23(88,5%) 3(11,5%) 0,866 Only Pelvic 53 (37,3%) 45(84,9%) 8(15,1%) Pelvic-Paraaortic 63 (44,4%) 52(82,5%) 11(17,5%) DM Yes 53 (37,3%) 44(83,0%) 9(17,0%) 0,894 No 89 (62,7%) 76(85,4%) 13(14,6%) HT Yes 65 (45,8%) 54(83,1%) 11(16,9%) 0,934 No 89 (54,2%) 66(74,2%) 11(25,8%) DM + HT Yes 34 (23,9%) 26(76,5%) 8(23,5%) 0,293 No 108 (76,1%) 94(87,0%) 14(13,0%) CAD Yes 13 (9,2%) 8(61,5%) 5(38,5%) 0,004 2,70 (1,43 − 10,63) No 129 (90,8%) 112(86,8%) 17(13,2%) Other Disease Yes 59 (41,5%) 46(78%) 13(22%) 0,047 2,31 (0,99 − 5,42) No 83 (58,5%) 74(89,2%) 9(10,8%) Grade Grade 1 104 (73,2%) 91(87,5%) 13(12,5%) 0,061 Grade 2 26 (18,3%) 18(69,2%) 8(30,8%) Grade 3 12 (8,5%) 11(91,7%) 1(8,3%) Tumor Size < 2 cm 27 (19%) 23(85,2%) 4(14,8%) 0,788 ≥ 2 cm 115 (81%) 97(84,3%) 18(15,7%) Mymetrial Invasion < 50% 95 (66,9%) 80(84,2%) 15(15,8%) 0,999 ≥ 50% 47 (33,1%) 40(85,1%) 7(14,9%) LVSI Yes 19 (13,4%) 18(94,7%) 1(5,3%) 0,200 No 123 (86,6%) 102(82,9%) 21(17,1%) Stromal Invasion Yes 20 (14,1%) 19(95,0%) 1(5,0%) 0,143 No 122 (85,9%) 101(82,8%) 21(17,2%) Pelvic Lymph Node Metastasis Yes 12 (8,5%) 11(91,7%) 1(8,3%) 0,522 No 130 (91,5%) 109(83,8%) 21(16,2%) Paraaortic Lymph Node Metastasis Yes 4 (2,8%) 3(75%) 1(25%) 0,162 No 138 (97,2%) 117(84,8%) 21(15,2%) Adnexal or Serosal Metastasis Yes 4 (2,8%) 4(100%) 0(0%) 0,438 No 138 (97,2%) 116(84,1%) 22(15,9%) Vaginal or Parametrial Metastasis Yes 2 (1,4%) 2(100%) 0(0%) 0,438 No 140 (98,6%) 118(84,3%) 22(15,7%) Distant Metastasis Yes 6 (4,2%) 5(83,3%) 1(16,7%) 0,878 No 136 (95,8%) 115(84,6%) 21(15,4%) LND: Lymph Node Dissection, DM: Diabetes Mellitus, HT: Hypertension, CAD: Coronary Artery Disease, LVSI: Lymphovasculer space involvement, Comprehensive analysis of inflammatory markers revealed significant associations with survival outcomes. The median values of key inflammatory markers were as follows: hemoglobin 12.90 g/dL (range: 8.10–16.70), hematocrit 39.0% (range: 4.0–50.0), white blood cell count 8.02 × 10³/µL (range: 2.77–15.91), lymphocyte count 2.19 × 10³/µL (range: 0.74–6.78), monocyte count 0.54 × 10³/µL (range: 0.17–1.12), neutrophil count 4.99 × 10³/µL (range: 0.92–11.05), and platelet count 286.50 × 10³/µL (range: 84.0-892.0). Advanced hematological parameters showed specific patterns associated with prognosis. The nucleated red blood cell percentage (NRBC%) had a median of 0.0% (range: 0.0-2.50) and showed significant prognostic value (HR: 2.80, 95% CI: 1.30–6.02, p = 0.002). The immature granulocyte percentage (IG%) had a median of 0.30% (range: 0.0-1.60) and was significantly associated with survival (HR: 3.882, 95% CI: 1.26–11.97, p = 0.017). The delta neutrophil index (DNI) had a median of 0.0052 (range: 0.0-0.0378) and showed prognostic significance when stratified at the cut-off value of 0.005763 (HR: 2.485, 95% CI: 1.011–5.644, p = 0.042). The median follow-up period was 28.0 months with an interquartile range of 16–37 months and a total range of 1–51 months. Overall survival analysis showed a mean survival time of 44.34 ± 1.3 months (95% CI: 41.84–46.85 months). Cumulative survival rates demonstrated excellent short-term outcomes with 96% ± 1.8% survival at 12 months, declining to 88% ± 3.1% at 24 months, 82% ± 3.9% at 36 months, and 72% ± 7.0% at 48 months. Of the 142 patients in the study cohort, 120 patients (84.5%) remained alive at the end of follow-up, while 22 patients (15.5%) had died. Age analysis revealed significant survival differences, with patients aged 65 years or older showing significantly worse outcomes compared to younger patients (HR: 2.70, 95% CI: 1.15–6.33, p = 0.017). Several inflammatory markers demonstrated significant prognostic value in univariate analysis. The systemic inflammatory response index (SIRI) showed strong prognostic significance when stratified at the cut-off value of 2.059, with patients having SIRI ≥ 2.059 showing significantly worse survival (HR: 2.485, 95% CI: 1.042–5.923, p = 0.034). Among 27 patients (19.0%) with elevated SIRI, 19 patients (70.4%) remained alive while 8 patients (29.6%) died during follow-up. The glucose-to-lymphocyte ratio (GLR) emerged as another significant prognostic factor when stratified at the cut-off value of 58.88. Patients with GLR ≥ 58.88 had significantly worse survival outcomes (HR: 2.841, 95% CI: 1.206–6.693, p = 0.013). Among 51 patients (35.9%) with elevated GLR, 38 patients (74.5%) remained alive while 13 patients (25.5%) died. The monocyte-to-lymphocyte ratio (MLR) showed prognostic significance at the cut-off value of 0.3, with elevated MLR associated with poor survival (HR: 2.579, 95% CI: 1.117–5.947, p = 0.021). Among 38 patients (26.8%) with MLR ≥ 0.3, 27 patients (71.1%) remained alive while 11 patients (28.9%) died. Other traditional inflammatory markers showed varying degrees of association with survival outcomes. The systemic immune-inflammation index (SII) was stratified at 1045.28, with patients having elevated SII showing a trend toward worse outcomes, though not reaching statistical significance in the final multivariate model. The pan-immune-inflammation value (PIV) was stratified at 337.23, showing similar trends but not maintaining significance in multivariate analysis. Multivariate Cox regression analysis identified several independent predictors of overall survival. Age remained a significant continuous variable with each year increase associated with a 6% increase in mortality risk (HR: 1.06, 95% CI: 1.01–1.11, p = 0.020). Among comorbidities, coronary artery disease emerged as a strong independent predictor of poor survival (HR: 2.70, 95% CI: 1.43–10.63, p = 0.004). The most significant inflammatory markers maintaining independence in multivariate analysis were SIRI ≥ 2.059 (HR: 2.485, 95% CI: 1.042–5.923, p = 0.034) and GLR ≥ 58.88 (HR: 2.841, 95% CI: 1.206–6.693, p = 0.013). These findings suggest that both markers provide independent prognostic information beyond traditional clinicopathological factors. Table 2 : Laboratory Parameters and Hematologic Indices with Survival Analysis Table 2 Laboratory Parameters and Hematologic Indices with Survival Analysis Variable Category Median(min-max) Total Alive status p HR (95% CI) Alive Ex HB 12,90(8,10–16,70) 142 (100%) 120(84,5%) 22(15,5%) 0,798 HCT 39,0(4,0–50,0) 142 (100%) 120(84,5%) 22(15,5%) 0,215 WBC 8,02(2,77 − 15,91) 142 (100%) 120(84,5%) 22(15,5%) 0,790 LYMPH 2,19(0,74 − 6,78) 142 (100%) 120(84,5%) 22(15,5%) 0,813 MONO 0,54(0,17 − 1,12) 142 (100%) 120(84,5%) 22(15,5%) 0,813 NEUT 4,99(0,92 − 11,05) 142 (100%) 120(84,5%) 22(15,5%) 0,795 PLT 286,50(84,0-892,0) 142 (100%) 120(84,5%) 22(15,5%) 0,881 NRBC% 0,0(0,0–2,50) 142 (100%) 120(84,5%) 22(15,5%) 0,002 2,80 (1,30 − 6,02) IG% 0,30(0,0–1,60) 142 (100%) 120(84,5%) 22(15,5%) 0,017 3,882 (1,26 − 11,97) DNI 0,0052(0,0–0,0378) 142 (100%) 120(84,5%) 22(15,5%) 0,003 8,2627E + 32(10.56 * 10⁹, 1.15*10⁵⁶) DNI < 0,005763 0,0042(0,0–0,0058) 81(57,0%) 73(90,1%) 8(9,9%) 0,042 2,485 (1,011 − 5,644) ≥ 0,005763 0,0074(0,0057 − 0,0378) 61(43,0%) 47(77,0%) 14(23%) CRP 4,99(0,0–34,25) N = 76 66(86,8%) 10(13,2%) 0,146 Ca125 17,95(4,70 − 24,80) N = 80 70(87,5%) 10(12,5%) 0,406 Ca19-9 27,40(0,80–568,80) N = 27 24(88,9%) 3(11,1%) 0,978 Ca15,3 14,40(4,80 − 25,80) N = 25 23(92%) 2(8%) 0,806 SII 635,89(143,11-2365,20) 142 (100%) 120(84,5%) 22(15,5%) 0,717 < 1045,28 572,78(143,11-1040,90) 113(79,6%) 94(83,2%) 19(16,8%) 0,545 ≥ 1045,28 1311,01(1045,28-2365,20) 29(20,4%) 26(89,7%) 3(10,3%) SIRI 1,24(0,16 − 4,80) 142 (100%) 120(84,5%) 22(15,5%) 0,394 < 2,059 1,11(0,16 − 2,06) 115(81,0%) 101(87,8%) 14(12,2%) 0,034 2,485 (1,042 − 5,923) ≥ 2,059 2,55(2,06 − 4,80) 27(19,0%) 19(70,4%) 8(29,6%) PIV 330,11(62,97-1541,15) 142 (100%) 120(84,5%) 22(15,5%) 0,913 < 337,23 238,92(62,97–336,84) 73(51,4%) 64(87,7%) 9(12,3%) 0,240 ≥ 337,23 560,36(337,23-1541,15) 69(48,6%) 56(81,2%) 1318,8(%) LMR 3,97(1,37 − 16,54) 142 (100%) 120(84,5%) 22(15,5%) 0,633 < 3,31 2,86(1,37 − 3,27) 36(25,4%) 26(72,2%) 10(27,8%) 0,051 ≥ 3,31 4,35(3,31 − 16,54) 106(74,6%) 94(88,7%) 12(11,3%) MLR 0,252(0,061 − 0,731) 142 (100%) 120(84,5%) 22(15,5%) 0,067 < 0,3 0,227(0,061 − 0,296) 104(73,2%) 93(89,4%) 11(10,6%) 0,021 2,579 (1,117-5,947) ≥ 0,3 0,341(0,30 − 0,731) 38(26,8%) 27(71,1%) 11(28,9%) NLR 2,40(0,58 − 9,73) 142 (100%) 120(84,5%) 22(15,5%) 0,383 < 2,85 2,03(0,58 − 2,81) 99(69,7%) 86(86,9%) 13(13,1%) 0,227 ≥ 2,85 3,53(2,85 − 9,73) 43(30,3%) 34(79,1%) 9(20,9%) PLR 130,08(60,09-381,75) 142 (100%) 120(84,5%) 22(15,5%) 0,514 < 150,26 114,30(60,09-149-14) 98(69,0%) 79(80,6%) 19(19,4%) 0,092 ≥ 150,26 187,66(150,26–381,75) 44(31,0%) 41(93,2%) 3(6,8%) ICPI 382,32(185,39-1090,05) 142 (100%) 120(84,5%) 22(15,5%) 0,517 < 438,85 340,02(185,39–436,94) 98(69,0%) 79(80,6%) 19(19,4%) 0,092 ≥ 438,85 454,97(438,85-1090,05) 44(31,0%) 41(93,2%) 3(6,8%) APRI 0,19(0,05 − 1,36) 142 (100%) 120(84,5%) 22(15,5%) 0,043 5,592 (0,975 − 32,045) < 0,37 0,18(0,05 − 0,36) 133(93,7%) 115(86,5%) 18(13,5%) 0,011 2,485 (1,251 − 11,061) ≥ 0,37 0,41(0,37 − 1,36) 9(6,3%) 5(55,6%) 4(44,4%) FIB4 0,96(0,38 − 8,27) 142 (100%) 120(84,5%) 22(15,5%) 0,004 2,80 (1,085 − 1,7821) < 1,13 0,79(0,38 − 1,12) 95(66,9%) 85(89,5%) 10(10,5%) 0,069 ≥ 1,13 1,44(1,13 − 8,27) 47(33,1%) 35(74,5%) 12(25,5%) IBI 12,17(0,0–76,78) N = 76 66(86,8%) 10(13,2%) 0,031 1,033 (1,0016 − 1,0664) < 19,98 7,69(0,0–17,96) 50(65,8%) 46(92,0%) 4(8,0%) 0,061 ≥ 19,98 35,88(19,98 − 76,78) 26(34,2%) 20(76,9%) 6(23,1%) GLR 51,92(16,23–304,0) 142 (100%) 120(84,5%) 22(15,5%) 0,616 < 58,88 43,96(16,23–58,36) 91(64,1%) 82(90,1%) 9(9,9%) 0,013 2,841 (1,206-6,693) ≥ 58,88 81,82(58,88–304,0) 51(35,9%) 38(74,5%) 13(25,5%) Discussion This study demonstrates that preoperative inflammatory markers, particularly the systemic inflammatory response index (SIRI) and glucose-to-lymphocyte ratio (GLR), serve as independent prognostic factors in patients with endometrioid-type endometrial cancer. By systematically excluding patients with adenomyosis and myoma uteri, we eliminated potential confounding effects of benign inflammatory conditions, providing a more accurate assessment of cancer-specific inflammatory processes. This methodological approach represents a novel contribution to the literature and strengthens the validity of our findings regarding the prognostic value of inflammatory markers in endometrial cancer. The SIRI emerged as one of the most significant independent predictors of survival in our cohort, with patients having SIRI ≥ 2.059 demonstrating significantly worse outcomes (HR: 2.485, 95% CI: 1.042–5.923, p = 0.034). This finding aligns with recent literature suggesting that SIRI may be superior to traditional inflammatory ratios due to its incorporation of three key immune cell populations: neutrophils, monocytes, and lymphocytes [ 8 ]. The prognostic value of SIRI likely reflects the complex interplay between tumor-promoting inflammation and anti-tumor immunity within the cancer microenvironment. Neutrophils and monocytes represent components of the innate immune system that can promote cancer progression through multiple mechanisms. Tumor-associated neutrophils can facilitate angiogenesis, promote metastasis through the release of neutrophil extracellular traps, and create an immunosuppressive microenvironment that impairs T-cell function [ 9 ]. Similarly, tumor-associated macrophages, primarily derived from circulating monocytes, can adopt an alternatively activated (M2) phenotype that promotes tumor growth, angiogenesis, and immune suppression [ 10 ]. Conversely, lymphocytes, particularly cytotoxic T-lymphocytes and natural killer cells, represent the adaptive immune response against malignancy and are generally associated with improved outcomes [ 11 ]. The incorporation of all three cell types in SIRI provides a more comprehensive assessment of the systemic immune-inflammatory balance compared to traditional two-component ratios. Recent studies in other malignancies have demonstrated the superiority of SIRI over NLR and PLR in predicting outcomes, supporting our findings in endometrial cancer [ 12 , 13 ]. The threshold of 2.059 identified in our study through ROC analysis provides a clinically applicable cut-off for risk stratification in routine clinical practice. The GLR emerged as another independent predictor of poor survival (HR: 2.841, 95% CI: 1.206–6.693, p = 0.013), representing a novel marker that combines metabolic and immune components. This finding highlights the important intersection between metabolic dysfunction and immune status in cancer prognosis. Elevated glucose levels may indicate insulin resistance, metabolic syndrome, or diabetes, all of which have been associated with poor outcomes in endometrial cancer through multiple mechanisms [ 14 ]. Hyperglycemia creates a tumor-permissive environment through several pathways. Elevated glucose provides metabolic fuel for rapidly proliferating cancer cells, which preferentially utilize glucose through aerobic glycolysis (the Warburg effect) [ 15 ]. Additionally, hyperglycemia can promote angiogenesis through advanced glycation end products and inflammatory pathways, facilitating tumor growth and metastasis [ 16 ]. Insulin resistance, often accompanying hyperglycemia, leads to elevated insulin and insulin-like growth factor-1 levels, which can directly stimulate cancer cell proliferation and survival [ 17 ]. The lymphocyte component of GLR reflects the host's adaptive immune response against the tumor. Lymphopenia in cancer patients often indicates immune suppression or exhaustion, which can result from tumor-derived immunosuppressive factors, chronic inflammation, or treatment-related effects [ 18 ]. The combination of elevated glucose and decreased lymphocytes captured by GLR therefore represents a dual mechanism of cancer progression: metabolic support for tumor growth coupled with impaired immune surveillance. Our finding that GLR ≥ 58.88 identifies high-risk patients suggests that interventions targeting both metabolic and immune components might be beneficial. Metformin, for example, has shown promise in endometrial cancer through both metabolic effects (improving insulin sensitivity and glucose metabolism) and direct anti-tumor properties [ 19 ]. Similarly, lifestyle interventions focusing on weight loss and glycemic control might improve outcomes in high-risk patients identified by elevated GLR. Our findings are consistent with the growing body of literature demonstrating the prognostic significance of inflammatory markers in endometrial cancer. Nishio et al., in their large multicenter Japanese study of 712 patients, found that NLR, PLR, and HALP scores were prognostic factors for both progression-free survival and overall survival [ 20 ]. However, their study included all histological subtypes and did not account for benign uterine conditions that might confound inflammatory marker interpretation. Ma et al. demonstrated that elevated NLR, PLR, and MLR predicted poor outcomes in 156 advanced endometrial cancer patients receiving radiotherapy, with their nomogram achieving high predictive accuracy (C-index: 0.995) [ 21 ]. Yanazume et al. found that NLR was an independent prognostic factor for both progression-free survival and overall survival in advanced endometrial cancer patients receiving immunotherapy, with optimal cut-off values of 4.92 and 5.40 respectively [ 22 ]. The meta-analysis by Leng et al., encompassing 14 studies with 5,274 patients, confirmed that pretreatment NLR and PLR were biomarkers of poor prognosis in endometrial cancer, with pooled hazard ratios of 2.51 for NLR and 2.50 for PLR regarding overall survival [ 23 ]. These findings support the clinical utility of inflammatory markers as prognostic tools in endometrial cancer management. Our study extends these findings by introducing novel markers like SIRI and GLR while addressing the important issue of confounding benign conditions. The exclusion of patients with adenomyosis and myoma uteri represents a methodological advancement that enhances the specificity of inflammatory marker assessment for cancer-related processes. This approach is particularly relevant given that adenomyosis affects up to 30% of women with endometrial cancer and can significantly elevate inflammatory markers through chronic inflammatory processes [ 24 ]. The prognostic value of inflammatory markers in endometrial cancer reflects several interconnected biological mechanisms. Cancer-related inflammation promotes tumor progression through multiple pathways including DNA damage from reactive oxygen species, stimulation of angiogenesis through inflammatory mediators, and activation of oncogenic signaling pathways such as NF-κB [ 25 ]. Systemic inflammation also contributes to cancer cachexia and reduces the effectiveness of anti-cancer treatments by impairing immune function and promoting treatment resistance [ 26 ]. The identification of high-risk patients through inflammatory markers has important therapeutic implications. Patients with elevated SIRI or GLR might benefit from more intensive surveillance protocols, consideration of adjuvant therapy even in early-stage disease, and enrollment in clinical trials investigating novel therapeutic approaches. Additionally, interventions targeting inflammation, such as aspirin or other anti-inflammatory agents, might improve outcomes in high-risk patients, though this requires prospective validation [ 27 ]. The metabolic component captured by GLR suggests that interventions targeting glucose metabolism and insulin resistance might be particularly beneficial in high-risk patients. Beyond metformin, other diabetes medications such as GLP-1 receptor agonists have shown anti-cancer properties in preclinical studies and might warrant investigation in endometrial cancer [ 28 ]. The inflammatory markers identified in our study could be readily implemented in clinical practice as they are derived from routine blood tests available in all healthcare settings. The calculation of SIRI and GLR is straightforward and could be automated in laboratory information systems or incorporated into electronic health records to provide real-time risk assessment. These markers could enhance current risk stratification systems by providing additional prognostic information beyond traditional histopathological factors. Patients identified as high-risk by inflammatory markers might benefit from more aggressive treatment approaches, including extended surgical staging, adjuvant therapy in earlier-stage disease, or enrollment in clinical trials investigating novel treatments. Conversely, low-risk patients might be candidates for de-escalated treatment approaches, reducing treatment-related morbidity while maintaining oncologic outcomes. The integration of inflammatory markers with molecular classification represents an exciting future direction. Recent advances in endometrial cancer classification based on molecular features (POLE mutations, microsatellite instability, p53 abnormalities) provide additional prognostic and predictive information [ 29 ]. Combining inflammatory markers with molecular features might create even more refined risk stratification tools for personalized treatment selection. Several limitations should be acknowledged in our study. The retrospective design introduces inherent selection bias and limits the ability to control for all potential confounding factors. The relatively small sample size of 142 patients, while adequate for initial validation, requires confirmation in larger multicenter cohorts. The single-center design may limit generalizability to other populations with different demographic characteristics, treatment patterns, or healthcare systems. The relatively short median follow-up of 28 months, while appropriate for initial survival analysis, limits assessment of long-term outcomes and late recurrences. Longer follow-up will be necessary to fully establish the prognostic value of these markers for disease-specific survival and to assess their utility in predicting late treatment effects or secondary malignancies. Missing data for some inflammatory markers, particularly C-reactive protein and tumor markers, prevented comprehensive analysis of all potentially relevant biomarkers. Future studies should include systematic collection of additional inflammatory and metabolic markers to create more comprehensive prognostic models. The lack of information about adjuvant treatment decisions and adherence represents another limitation, as these factors could influence survival outcomes independently of inflammatory markers. Future studies should systematically collect treatment data to assess whether inflammatory markers predict treatment response or guide treatment selection. Future research should focus on several key areas to advance the clinical utility of inflammatory markers in endometrial cancer. Large-scale multicenter prospective studies are needed to validate our findings and establish standardized cut-off values applicable across different populations and laboratory systems. These studies should include diverse patient populations to ensure generalizability and should systematically collect comprehensive clinical, pathological, and treatment data. Longitudinal assessment of inflammatory marker changes during treatment represents an important research direction. Serial measurements of SIRI, GLR, and other markers during chemotherapy, radiation therapy, or immunotherapy might provide dynamic prognostic information and help guide treatment modifications. Understanding how these markers change in response to successful treatment versus disease progression could enhance their clinical utility. Investigation of inflammatory markers in other endometrial cancer subtypes, including serous, clear cell, and carcinosarcoma variants, is needed to determine whether the prognostic value extends beyond endometrioid cancers. These aggressive subtypes often have different biological characteristics and treatment responses, and inflammatory markers might provide different prognostic information. The integration of inflammatory markers with molecular classification and other novel biomarkers represents a promising future direction. Combining traditional histopathological factors, molecular features, and inflammatory markers might create comprehensive prognostic models that better predict individual patient outcomes and guide personalized treatment selection. Development of targeted interventions based on inflammatory marker status should be a priority for future clinical trials. Patients with elevated inflammatory markers might benefit from anti-inflammatory treatments, metabolic interventions, or immunomodulatory therapies. Prospective trials testing these interventions in biomarker-selected populations could demonstrate the therapeutic utility of inflammatory marker assessment. Conclusions This study demonstrates that preoperative inflammatory markers, particularly the systemic inflammatory response index (SIRI) and glucose-to-lymphocyte ratio (GLR), serve as independent prognostic factors in patients with endometrioid-type endometrial cancer. By systematically excluding patients with adenomyosis and myoma uteri, we provide evidence that these markers reflect cancer-specific inflammatory processes rather than benign inflammatory conditions. The SIRI ≥ 2.059 and GLR ≥ 58.88 thresholds identify high-risk patients who might benefit from intensified surveillance, consideration of adjuvant therapy, or enrollment in clinical trials investigating novel therapeutic approaches. These readily available, cost-effective biomarkers derived from routine blood tests could enhance current risk stratification systems by providing additional prognostic information beyond traditional histopathological factors. The combination of inflammatory, metabolic, and immune components captured by these markers offers insights into the complex biological processes underlying endometrial cancer progression and suggests potential therapeutic targets for improving patient outcomes. However, validation in larger, prospective, multicenter studies is essential before clinical implementation. Future research should focus on establishing standardized cut-off values, investigating the utility of these markers in other endometrial cancer subtypes, and developing targeted interventions for high-risk patients identified by inflammatory marker assessment. The findings support the growing recognition that systemic inflammation plays a crucial role in cancer progression and that simple blood-based markers can provide valuable prognostic information complementing traditional pathological factors in the era of personalized cancer medicine. Declarations Conflict of Interest The authors have no relevant financial or non-financial interests to disclose. 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 2024/532 dated 05/06/2024). Approval for publication Not applicable Code usability Can be used Funding Not applicable Author Contribution GU, KA and HY developed the concept and were responsible for data collection. SGG, GU and PA planned the study. GU, KA and HY analysed the results. GU, TTI and PA 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 Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73. Concin N, Matias-Guiu X, Vergote I, Cibula D, Mirza MR, Marnitz S, et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer. 2021;31:12–39. Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15:e493–503. Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140:883–99. Templeton AJ, McNamara MG, Šeruga B, Vera-Badillo FE, Aneja P, Ocaña A, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014;106:dju124. Xiao Y, Zheng Y, Tu Y, Tian C, Yu J, Lin H et al. Nomogram incorporating inflammatory index, pathology, and molecular classification for predicting recurrence in patients with stage I-III endometrial cancer: a multi-institutional study. J Inflamm Res. 2025;:10559–72. Qi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122:2158–67. Rajakumar HK, Sathyabal VC, Vasanthan M, Dasarathan R. The predictive role of Systemic Inflammation Response Index (SIRI), Neutrophil-Lymphocyte Ratio (NLR), and Platelet-Lymphocyte Ratio (PLR) in the prognosis of acute coronary syndrome in a tertiary care hospital. Heliyon. 2024;10. Coffelt SB, Wellenstein MD, de Visser KE. Neutrophils in cancer: neutral no more. Nat Rev Cancer. 2016;16:431–46. Mantovani A, Marchesi F, Malesci A, Laghi L, Allavena P. Tumour-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol. 2017;14:399–416. Zitvogel L, Galluzzi L, Kepp O, Smyth MJ, Kroemer G. Type I interferons in anticancer immunity. Nat Rev Immunol. 2015;15:405–14. Al Maqrashi Z, Bradbury M, Chan SWS, AlHarbi Y, Fazzari F, Gabara A, et al. Prognostic factors in advanced incurable HNSCC patients on palliative-intent immunotherapy-based regimen. Future Sci OA. 2025;11:2552067. Zhou T, Fang J, Huang J, Yu X, Shan Y, Wu S, et al. Prognostic Value of Inflammatory Markers in HBV-Related HCC After Hepatectomy Based on a Clinical Database. J Invest Surg. 2025;38:2475020. Mu N, Zhu Y, Wang Y, Zhang H, Xue F. Insulin resistance: a significant risk factor of endometrial cancer. Gynecol Oncol. 2012;125:751–7. Warburg O. On the origin of cancer cells. Science (1979). 1956;123:309–14. Ryu TY, Park J, Scherer PE. Hyperglycemia as a risk factor for cancer progression. Diabetes Metab J. 2014;38:330. Gallagher EJ, LeRoith D. Obesity and diabetes: the increased risk of cancer and cancer-related mortality. Physiol Rev. 2015;95:727–48. Lissoni P, Brivio F, Fumagalli L, Messina G, Ghezzi V, Frontini L, et al. Efficacy of cancer chemotherapy in relation to the pretreatment number of lymphocytes in patients with metastatic solid tumors. Int J Biol Markers. 2004;19:135–40. Cardel M, Jensen SM, Pottegård A, Jørgensen TL, Hallas J. Long-term use of metformin and colorectal cancer risk in type II diabetics: a population‐based case–control study. Cancer Med. 2014;3:1458–66. Nishio S, Murotani K, Yamagami W, Suzuki S, Nakai H, Kato K, et al. Pretreatment systemic inflammatory markers predict survival in endometrial cancer: A Japanese Gynecologic Oncology Group 2043 exploratory data analysis. Gynecol Oncol. 2024;181:46–53. 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;15:966. Yanazume S, Nagata C, Kobayashi Y, Fukuda M, Mizuno M, Togami S, et al. Potential prognostic predictors of treatment with immune checkpoint inhibitors for advanced endometrial cancer. Jpn J Clin Oncol. 2025;55:29–35. Leng J, Wu F, Zhang L. Prognostic Significance of Pretreatment Neutrophil-to-Lymphocyte Ratio, Platelet – to – Lymphocyte Ratio, or Monocyte-to-Lymphocyte Ratio in Endometrial Neoplasms: A Systematic Review and Meta – analysis. Front Oncol. 2022;12:734948. Raffone A, Seracchioli R, Raimondo D, Maletta M, Travaglino A, Raimondo I, et al. Prevalence of adenomyosis in endometrial cancer patients: a systematic review and meta-analysis. Arch Gynecol Obstet. 2021;303:47–53. Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454:436–44. Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer. 2014;14:754–62. Rothwell PM, Wilson M, Elwin C-E, Norrving B, Algra A, Warlow CP, et al. Long-term effect of aspirin on colorectal cancer incidence and mortality: 20-year follow-up of five randomised trials. Lancet. 2010;376:1741–50. Bourou MZ, Matsas A, Valsamakis G, Vlahos N, Panoskaltsis T, BOUROU MZOI et al. The potential role of glucagon-like peptide-1 (GLP-1) receptor agonists as a type of conservative treatment of endometrial cancer in women of reproductive age: a review of the literature and a call for study. Cureus. 2024;16. León-Castillo A, De Boer SM, Powell ME, Mileshkin LR, Mackay HJ, Leary A, et al. Molecular classification of the PORTEC-3 trial for high-risk endometrial cancer: impact on prognosis and benefit from adjuvant therapy. J Clin Oncol. 2020;38:3388–97. 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-8414305","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580901457,"identity":"529cff74-024a-42a2-966a-0c606f2ce912","order_by":0,"name":"Gorkem ULGER","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Gorkem","middleName":"","lastName":"ULGER","suffix":""},{"id":580901458,"identity":"fdc59360-71bb-4457-97e7-4e6e373303fd","order_by":1,"name":"Kasim AKAY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACCSA+wMCQwMAgf/7jAyCHh494LRIMxgYgLWzEaGGAajEDcwhqkWw//vDQjZo0eYPbDWmVX3PsZNgYmB8+uoFHizRPjsHhnGM5hhvuHDh2W3ZbMtBhbMbGOXi0yDHkMBzOYatg3HAgse225DZmoBYeNmm8WvifPzic86/CfsOBZLZiyW31hLVISyQYHM5ty0nccCONjfHjtsOEtUjOeAPU0peWPPPMGWZpxm3HediYCfhF4nz6488535Jt+473MH78ua3anp+9+eFjfFrgQOEAAwMzD4jFTIxyEJBvYGBg/EGs6lEwCkbBKBhRAAArO0zgrul4UAAAAABJRU5ErkJggg==","orcid":"","institution":"Duzici State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Kasim","middleName":"","lastName":"AKAY","suffix":""},{"id":580901459,"identity":"0581322f-f58a-4b26-9d78-62c860eb9646","order_by":2,"name":"Hamza YILDIZ","email":"","orcid":"","institution":"Tarsus State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hamza","middleName":"","lastName":"YILDIZ","suffix":""},{"id":580901460,"identity":"c0421782-09ef-40e3-825a-36316620861c","order_by":3,"name":"Pelin AYTAN","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Pelin","middleName":"","lastName":"AYTAN","suffix":""},{"id":580901461,"identity":"959c031e-2e56-45ec-aa4e-346ad430a1e9","order_by":4,"name":"Sevki Goksun GOKULU","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Sevki","middleName":"Goksun","lastName":"GOKULU","suffix":""},{"id":580901462,"identity":"169e67b8-476b-42ef-9c3f-ebadb9cbd54f","order_by":5,"name":"Tolgay Tuyan ILHAN","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Tolgay","middleName":"Tuyan","lastName":"ILHAN","suffix":""}],"badges":[],"createdAt":"2025-12-20 21:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8414305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8414305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101312695,"identity":"be0ceb96-0c6e-4b43-b8ab-f560ef36c25a","added_by":"auto","created_at":"2026-01-28 11:12:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1008278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8414305/v1/00b76089-5c99-45ad-90b0-143a04d68151.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of Systemic Inflammatory Markers in Patients with Endometrioid-Type Endometrial Cancer: A Retrospective Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer is the most common gynecologic malignancy in developed countries, with endometrioid-type adenocarcinoma representing approximately 80% of all cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite generally favorable outcomes for early-stage disease, accurate prognostic assessment remains crucial for treatment planning and patient counseling. Traditional prognostic factors include histologic grade, depth of myometrial invasion, lymphovascular space invasion, and lymph node status [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe tumor microenvironment and host immune response play critical roles in cancer progression and patient outcomes. Systemic inflammatory markers derived from routine blood tests have emerged as cost-effective prognostic tools across various malignancies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) have been extensively studied as markers of systemic inflammation and immune dysfunction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNovel inflammatory indices such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation value (PIV) have been proposed as potentially superior prognostic markers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The SIRI, calculated as neutrophil count multiplied by monocyte count divided by lymphocyte count, incorporates three key immune cell populations and may provide a more comprehensive assessment of systemic inflammation than traditional ratios [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, their utility in endometrial cancer, particularly in the endometrioid subtype, remains understudied.\u003c/p\u003e \u003cp\u003eThis study aims to evaluate the prognostic value of comprehensive inflammatory markers in a carefully selected cohort of patients with endometrioid-type endometrial cancer, excluding those with adenomyosis and myoma uteri to minimize inflammatory confounders and provide more accurate assessment of cancer-related inflammatory status.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis retrospective study was conducted at the Gynecological Oncology Clinic, Department of Obstetrics and Gynecology, Mersin University Faculty of Medicine Hospital. The study protocol was approved by the Mersin University Clinical Research Ethics Committee (Decision No: 2024/532, June 5, 2024) and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eFrom a total of 195 patients who underwent surgery for endometrioid-type endometrial cancer between 2018 and 2024, 142 patients met the inclusion criteria and were included in the final analysis. The patient selection process involved careful screening to exclude confounding factors that could influence inflammatory marker interpretation.\u003c/p\u003e \u003cp\u003eThe inclusion criteria comprised patients aged 18 years or older with histologically confirmed endometrioid-type endometrial cancer who underwent complete surgical staging with available preoperative blood count data within 7 days before surgery and complete follow-up data. The exclusion criteria were designed to eliminate potential confounders of inflammatory markers and included non-endometrioid histological subtypes, previous surgery for endometrial cancer, prior chemotherapy or radiotherapy, coexistent adenomyosis confirmed on final histopathology, coexistent uterine myomas (leiomyomas) confirmed on final histopathology, active inflammatory conditions or infections, hematological disorders, insufficient medical records, and age less than 18 years.\u003c/p\u003e \u003cp\u003eThe patient selection process began with 195 endometrioid-type endometrial cancer patients who underwent surgery during the study period. Following systematic application of exclusion criteria, patients with adenomyosis were excluded based on final histopathological examination showing ectopic endometrial glands and stroma invading the myometrium at least 2.5 mm beyond the endometrial-myometrial junction. Patients with myoma uteri were excluded when final histopathology confirmed the presence of leiomyomas. Additional exclusions included patients with incomplete data, those with other exclusion criteria, resulting in the final study cohort of 142 patients.\u003c/p\u003e \u003cp\u003ePatient demographics, clinical characteristics, surgical details, and histopathological findings were retrospectively collected from medical records and the electronic patient registration system. All histopathological specimens were reviewed by experienced gynecologic pathologists at our institution using standardized criteria. Tumor staging was performed according to the FIGO 2023 system, and tumor grading followed WHO classification guidelines.\u003c/p\u003e \u003cp\u003ePreoperative blood samples were obtained within 7 days before surgery after overnight fasting and analyzed using automated hematology analyzers. Complete blood count parameters including white blood cell count, neutrophil count, lymphocyte count, monocyte count, platelet count, and hemoglobin levels were recorded. Additional laboratory parameters including glucose levels, liver function tests, and when available, C-reactive protein and tumor markers were documented.\u003c/p\u003e \u003cp\u003eVarious inflammatory markers were calculated using established formulas. The neutrophil-to-lymphocyte ratio (NLR) was calculated as neutrophil count divided by lymphocyte count. The platelet-to-lymphocyte ratio (PLR) was calculated as platelet count divided by lymphocyte count. The monocyte-to-lymphocyte ratio (MLR) was calculated as monocyte count divided by lymphocyte count. The systemic immune-inflammation index (SII) was calculated as neutrophil count multiplied by platelet count divided by lymphocyte count. The systemic inflammatory response index (SIRI) was calculated as neutrophil count multiplied by monocyte count divided by lymphocyte count. The pan-immune-inflammation value (PIV) was calculated as neutrophil count multiplied by platelet count multiplied by monocyte count divided by lymphocyte count. The lymphocyte-to-monocyte ratio (LMR) was calculated as lymphocyte count divided by monocyte count. The glucose-to-lymphocyte ratio (GLR) was calculated as glucose level divided by lymphocyte count.\u003c/p\u003e \u003cp\u003e All patients underwent comprehensive surgical staging according to established guidelines. The surgical approach included total hysterectomy and bilateral salpingo-oophorectomy. Lymph node dissection decisions were based on intraoperative frozen section analysis and established criteria. Pelvic lymphadenectomy was performed for Grade 1\u0026ndash;2 tumors with diameter greater than 2 cm, myometrial invasion greater than 50%, or cervical stromal involvement. Para-aortic lymphadenectomy was performed for Grade 3 tumors regardless of other factors, and for selected Grade 1\u0026ndash;2 cases based on intraoperative findings. The extent of lymph node dissection was determined by the surgical team based on individual patient characteristics and institutional protocols.\u003c/p\u003e \u003cp\u003eStatistical analysis was performed using SPSS 22.0 software package. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data or median with minimum and maximum values for non-normally distributed data. Categorical variables were presented as frequencies and percentages. The normality of continuous variables was assessed using the Kolmogorov-Smirnov test.\u003c/p\u003e \u003cp\u003eOptimal cut-off values for inflammatory markers were determined using receiver operating characteristic (ROC) curve analysis, with the Youden index (sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1) used to identify the most discriminating threshold. Survival analysis was conducted using the Kaplan-Meier method with log-rank test for comparisons between groups. Univariate and multivariate Cox proportional hazards regression models were used to identify independent prognostic factors for overall survival. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis were included in multivariate models. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 142 patients with endometrioid-type endometrial cancer were included in the analysis. The mean age was 59.75\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01 years. Patient demographics showed that 95 patients (66.9%) were younger than 65 years, while 47 patients (33.1%) were 65 years or older. The median gravidity was 2.0 (range: 0\u0026ndash;11), and median parity was 2.0 (range: 0\u0026ndash;11).\u003c/p\u003e \u003cp\u003eRegarding surgical approach, 93 patients (65.5%) underwent laparotomy while 49 patients (34.5%) had laparoscopic surgery. Lymph node dissection patterns varied based on risk factors, with 26 patients (18.3%) not undergoing lymph node dissection, 53 patients (37.3%) having only pelvic lymphadenectomy, and 63 patients (44.4%) receiving both pelvic and para-aortic lymphadenectomy.\u003c/p\u003e \u003cp\u003eComorbidity analysis revealed that 53 patients (37.3%) had diabetes mellitus, 65 patients (45.8%) had hypertension, 34 patients (23.9%) had both diabetes and hypertension, 13 patients (9.2%) had coronary artery disease, and 59 patients (41.5%) had other additional diseases. These comorbidities were considered in the analysis as potential confounders of inflammatory marker interpretation.\u003c/p\u003e \u003cp\u003eHistopathological examination showed that the majority of tumors were well-differentiated, with 104 patients (73.2%) having Grade 1 tumors, 26 patients (18.3%) having Grade 2 tumors, and 12 patients (8.5%) having Grade 3 tumors. Tumor size analysis revealed that 27 patients (19%) had tumors smaller than 2 cm, while 115 patients (81%) had tumors 2 cm or larger.\u003c/p\u003e \u003cp\u003eMyometrial invasion assessment showed that 95 patients (66.9%) had invasion less than 50% of myometrial thickness, while 47 patients (33.1%) had invasion of 50% or greater. Lymphovascular space invasion (LVSI) was present in 19 patients (13.4%) and stromal invasion was detected in 20 patients (14.1%).\u003c/p\u003e \u003cp\u003eAssessment of extrauterine spread showed that pelvic involvement was found in 12 patients (8.5%), para-aortic involvement in 4 patients (2.8%), serosa and adnexal involvement in 4 patients (2.8%), vaginal-parametrial involvement in 2 patients (1.4%), and distant metastasis in 6 patients (4.2%). The majority of patients (130 patients, 91.5%) had no pelvic involvement, and 138 patients (97.2%) had no para-aortic involvement.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Patient Demographics, Clinical Characteristics, and Survival Analysis\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Demographics, Clinical Characteristics, and Survival Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD/ Median(min-max)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAlive Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,75\u0026thinsp;\u0026plusmn;\u0026thinsp;9,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e(1,01\u0026ndash;1,11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,98\u0026thinsp;\u0026plusmn;\u0026thinsp;5,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (66,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86(90,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(9,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,70\u003c/p\u003e \u003cp\u003e(1,15\u0026thinsp;\u0026minus;\u0026thinsp;6,33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69,77\u0026thinsp;\u0026plusmn;\u0026thinsp;4,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (33,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(72,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(27,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,0 (0\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,0 (0\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaparotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (65,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74(79,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19(20,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaparoscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (34,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(93,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(6,1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Done\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (18,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(88,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(11,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnly Pelvic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (37,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(84,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(15,1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePelvic-Paraaortic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (44,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52(82,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(17,5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (37,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(83,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(17,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (62,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76(85,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(14,6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (45,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54(83,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(16,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (54,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(74,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(25,8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDM\u0026thinsp;+\u0026thinsp;HT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (23,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(76,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(23,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (76,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94(87,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13,0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (9,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(61,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(38,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,70\u003c/p\u003e \u003cp\u003e(1,43\u0026thinsp;\u0026minus;\u0026thinsp;10,63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129 (90,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112(86,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17(13,2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOther Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (41,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,31\u003c/p\u003e \u003cp\u003e(0,99\u0026thinsp;\u0026minus;\u0026thinsp;5,42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (58,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74(89,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(10,8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (73,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91(87,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(12,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (18,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(69,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(30,8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (8,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(91,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(8,3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTumor Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(85,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(14,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(84,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18(15,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMymetrial Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (66,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80(84,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(15,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (33,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(85,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(14,9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLVSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (13,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(94,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(5,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (86,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102(82,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(17,1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStromal Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (14,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(95,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(5,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (85,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101(82,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(17,2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePelvic Lymph Node Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (8,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(91,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(8,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (91,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109(83,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(16,2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParaaortic Lymph Node Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (2,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (97,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117(84,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(15,2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdnexal or Serosal Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (2,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (97,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116(84,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVaginal or Parametrial Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140 (98,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118(84,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDistant Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (4,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(83,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(16,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (95,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115(84,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(15,4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eLND: Lymph Node Dissection, DM: Diabetes Mellitus, HT: Hypertension, CAD: Coronary Artery Disease, LVSI: Lymphovasculer space involvement,\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComprehensive analysis of inflammatory markers revealed significant associations with survival outcomes. The median values of key inflammatory markers were as follows: hemoglobin 12.90 g/dL (range: 8.10\u0026ndash;16.70), hematocrit 39.0% (range: 4.0\u0026ndash;50.0), white blood cell count 8.02 \u0026times; 10\u0026sup3;/\u0026micro;L (range: 2.77\u0026ndash;15.91), lymphocyte count 2.19 \u0026times; 10\u0026sup3;/\u0026micro;L (range: 0.74\u0026ndash;6.78), monocyte count 0.54 \u0026times; 10\u0026sup3;/\u0026micro;L (range: 0.17\u0026ndash;1.12), neutrophil count 4.99 \u0026times; 10\u0026sup3;/\u0026micro;L (range: 0.92\u0026ndash;11.05), and platelet count 286.50 \u0026times; 10\u0026sup3;/\u0026micro;L (range: 84.0-892.0).\u003c/p\u003e \u003cp\u003eAdvanced hematological parameters showed specific patterns associated with prognosis. The nucleated red blood cell percentage (NRBC%) had a median of 0.0% (range: 0.0-2.50) and showed significant prognostic value (HR: 2.80, 95% CI: 1.30\u0026ndash;6.02, p\u0026thinsp;=\u0026thinsp;0.002). The immature granulocyte percentage (IG%) had a median of 0.30% (range: 0.0-1.60) and was significantly associated with survival (HR: 3.882, 95% CI: 1.26\u0026ndash;11.97, p\u0026thinsp;=\u0026thinsp;0.017). The delta neutrophil index (DNI) had a median of 0.0052 (range: 0.0-0.0378) and showed prognostic significance when stratified at the cut-off value of 0.005763 (HR: 2.485, 95% CI: 1.011\u0026ndash;5.644, p\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e \u003cp\u003eThe median follow-up period was 28.0 months with an interquartile range of 16\u0026ndash;37 months and a total range of 1\u0026ndash;51 months. Overall survival analysis showed a mean survival time of 44.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 months (95% CI: 41.84\u0026ndash;46.85 months). Cumulative survival rates demonstrated excellent short-term outcomes with 96% \u0026plusmn; 1.8% survival at 12 months, declining to 88% \u0026plusmn; 3.1% at 24 months, 82% \u0026plusmn; 3.9% at 36 months, and 72% \u0026plusmn; 7.0% at 48 months.\u003c/p\u003e \u003cp\u003eOf the 142 patients in the study cohort, 120 patients (84.5%) remained alive at the end of follow-up, while 22 patients (15.5%) had died. Age analysis revealed significant survival differences, with patients aged 65 years or older showing significantly worse outcomes compared to younger patients (HR: 2.70, 95% CI: 1.15\u0026ndash;6.33, p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e \u003cp\u003eSeveral inflammatory markers demonstrated significant prognostic value in univariate analysis. The systemic inflammatory response index (SIRI) showed strong prognostic significance when stratified at the cut-off value of 2.059, with patients having SIRI\u0026thinsp;\u0026ge;\u0026thinsp;2.059 showing significantly worse survival (HR: 2.485, 95% CI: 1.042\u0026ndash;5.923, p\u0026thinsp;=\u0026thinsp;0.034). Among 27 patients (19.0%) with elevated SIRI, 19 patients (70.4%) remained alive while 8 patients (29.6%) died during follow-up.\u003c/p\u003e \u003cp\u003eThe glucose-to-lymphocyte ratio (GLR) emerged as another significant prognostic factor when stratified at the cut-off value of 58.88. Patients with GLR\u0026thinsp;\u0026ge;\u0026thinsp;58.88 had significantly worse survival outcomes (HR: 2.841, 95% CI: 1.206\u0026ndash;6.693, p\u0026thinsp;=\u0026thinsp;0.013). Among 51 patients (35.9%) with elevated GLR, 38 patients (74.5%) remained alive while 13 patients (25.5%) died.\u003c/p\u003e \u003cp\u003eThe monocyte-to-lymphocyte ratio (MLR) showed prognostic significance at the cut-off value of 0.3, with elevated MLR associated with poor survival (HR: 2.579, 95% CI: 1.117\u0026ndash;5.947, p\u0026thinsp;=\u0026thinsp;0.021). Among 38 patients (26.8%) with MLR\u0026thinsp;\u0026ge;\u0026thinsp;0.3, 27 patients (71.1%) remained alive while 11 patients (28.9%) died.\u003c/p\u003e \u003cp\u003eOther traditional inflammatory markers showed varying degrees of association with survival outcomes. The systemic immune-inflammation index (SII) was stratified at 1045.28, with patients having elevated SII showing a trend toward worse outcomes, though not reaching statistical significance in the final multivariate model. The pan-immune-inflammation value (PIV) was stratified at 337.23, showing similar trends but not maintaining significance in multivariate analysis.\u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis identified several independent predictors of overall survival. Age remained a significant continuous variable with each year increase associated with a 6% increase in mortality risk (HR: 1.06, 95% CI: 1.01\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.020). Among comorbidities, coronary artery disease emerged as a strong independent predictor of poor survival (HR: 2.70, 95% CI: 1.43\u0026ndash;10.63, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003eThe most significant inflammatory markers maintaining independence in multivariate analysis were SIRI\u0026thinsp;\u0026ge;\u0026thinsp;2.059 (HR: 2.485, 95% CI: 1.042\u0026ndash;5.923, p\u0026thinsp;=\u0026thinsp;0.034) and GLR\u0026thinsp;\u0026ge;\u0026thinsp;58.88 (HR: 2.841, 95% CI: 1.206\u0026ndash;6.693, p\u0026thinsp;=\u0026thinsp;0.013). These findings suggest that both markers provide independent prognostic information beyond traditional clinicopathological factors.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Laboratory Parameters and Hematologic Indices with Survival Analysis\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLaboratory Parameters and Hematologic Indices with Survival Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian(min-max)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAlive status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,90(8,10\u0026ndash;16,70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,0(4,0\u0026ndash;50,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,02(2,77\u0026thinsp;\u0026minus;\u0026thinsp;15,91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYMPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,19(0,74\u0026thinsp;\u0026minus;\u0026thinsp;6,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMONO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,54(0,17\u0026thinsp;\u0026minus;\u0026thinsp;1,12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,99(0,92\u0026thinsp;\u0026minus;\u0026thinsp;11,05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286,50(84,0-892,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRBC%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0(0,0\u0026ndash;2,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,80\u003c/p\u003e \u003cp\u003e(1,30\u0026thinsp;\u0026minus;\u0026thinsp;6,02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIG%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,30(0,0\u0026ndash;1,60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,882\u003c/p\u003e \u003cp\u003e(1,26\u0026thinsp;\u0026minus;\u0026thinsp;11,97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0052(0,0\u0026ndash;0,0378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,2627E\u0026thinsp;+\u0026thinsp;32(10.56 * 10⁹, 1.15*10⁵⁶)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,005763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0042(0,0\u0026ndash;0,0058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81(57,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(90,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(9,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,485\u003c/p\u003e \u003cp\u003e(1,011\u0026thinsp;\u0026minus;\u0026thinsp;5,644)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0,005763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0074(0,0057\u0026thinsp;\u0026minus;\u0026thinsp;0,0378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(43,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47(77,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,99(0,0\u0026ndash;34,25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(86,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(13,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,95(4,70\u0026thinsp;\u0026minus;\u0026thinsp;24,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(87,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(12,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa19-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27,40(0,80\u0026ndash;568,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(88,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(11,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa15,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,40(4,80\u0026thinsp;\u0026minus;\u0026thinsp;25,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e635,89(143,11-2365,20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1045,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572,78(143,11-1040,90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113(79,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94(83,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19(16,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1045,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1311,01(1045,28-2365,20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(20,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(89,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(10,3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,24(0,16\u0026thinsp;\u0026minus;\u0026thinsp;4,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,11(0,16\u0026thinsp;\u0026minus;\u0026thinsp;2,06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115(81,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101(87,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(12,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,485\u003c/p\u003e \u003cp\u003e(1,042\u0026thinsp;\u0026minus;\u0026thinsp;5,923)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,55(2,06\u0026thinsp;\u0026minus;\u0026thinsp;4,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(19,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(70,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(29,6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330,11(62,97-1541,15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;337,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238,92(62,97\u0026ndash;336,84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(51,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64(87,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(12,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;337,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e560,36(337,23-1541,15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(48,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56(81,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1318,8(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,97(1,37\u0026thinsp;\u0026minus;\u0026thinsp;16,54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,86(1,37\u0026thinsp;\u0026minus;\u0026thinsp;3,27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(25,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(72,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(27,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,35(3,31\u0026thinsp;\u0026minus;\u0026thinsp;16,54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106(74,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94(88,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(11,3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,252(0,061\u0026thinsp;\u0026minus;\u0026thinsp;0,731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,227(0,061\u0026thinsp;\u0026minus;\u0026thinsp;0,296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104(73,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93(89,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(10,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,579\u003c/p\u003e \u003cp\u003e(1,117-5,947)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,341(0,30\u0026thinsp;\u0026minus;\u0026thinsp;0,731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(26,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(71,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(28,9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,40(0,58\u0026thinsp;\u0026minus;\u0026thinsp;9,73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,03(0,58\u0026thinsp;\u0026minus;\u0026thinsp;2,81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99(69,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86(86,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(13,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,53(2,85\u0026thinsp;\u0026minus;\u0026thinsp;9,73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(30,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(79,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(20,9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130,08(60,09-381,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114,30(60,09-149-14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98(69,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79(80,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19(19,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;150,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187,66(150,26\u0026ndash;381,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(31,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41(93,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(6,8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e382,32(185,39-1090,05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;438,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340,02(185,39\u0026ndash;436,94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98(69,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79(80,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19(19,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;438,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454,97(438,85-1090,05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(31,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41(93,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(6,8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,19(0,05\u0026thinsp;\u0026minus;\u0026thinsp;1,36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e 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colname=\"c6\"\u003e \u003cp\u003e18(13,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,485\u003c/p\u003e \u003cp\u003e(1,251\u0026thinsp;\u0026minus;\u0026thinsp;11,061)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,41(0,37\u0026thinsp;\u0026minus;\u0026thinsp;1,36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(6,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(55,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(44,4%)\u003c/p\u003e \u003c/td\u003e 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rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,79(0,38\u0026thinsp;\u0026minus;\u0026thinsp;1,12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(66,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85(89,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(10,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,44(1,13\u0026thinsp;\u0026minus;\u0026thinsp;8,27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(33,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(74,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(25,5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,17(0,0\u0026ndash;76,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(86,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(13,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,033\u003c/p\u003e \u003cp\u003e(1,0016\u0026thinsp;\u0026minus;\u0026thinsp;1,0664)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;19,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,69(0,0\u0026ndash;17,96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(65,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(92,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(8,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;19,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,88(19,98\u0026thinsp;\u0026minus;\u0026thinsp;76,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(34,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(76,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(23,1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51,92(16,23\u0026ndash;304,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(84,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(15,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;58,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43,96(16,23\u0026ndash;58,36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91(64,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(90,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(9,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2,841\u003c/p\u003e \u003cp\u003e(1,206-6,693)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;58,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81,82(58,88\u0026ndash;304,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(35,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(74,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(25,5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that preoperative inflammatory markers, particularly the systemic inflammatory response index (SIRI) and glucose-to-lymphocyte ratio (GLR), serve as independent prognostic factors in patients with endometrioid-type endometrial cancer. By systematically excluding patients with adenomyosis and myoma uteri, we eliminated potential confounding effects of benign inflammatory conditions, providing a more accurate assessment of cancer-specific inflammatory processes. This methodological approach represents a novel contribution to the literature and strengthens the validity of our findings regarding the prognostic value of inflammatory markers in endometrial cancer.\u003c/p\u003e \u003cp\u003eThe SIRI emerged as one of the most significant independent predictors of survival in our cohort, with patients having SIRI\u0026thinsp;\u0026ge;\u0026thinsp;2.059 demonstrating significantly worse outcomes (HR: 2.485, 95% CI: 1.042\u0026ndash;5.923, p\u0026thinsp;=\u0026thinsp;0.034). This finding aligns with recent literature suggesting that SIRI may be superior to traditional inflammatory ratios due to its incorporation of three key immune cell populations: neutrophils, monocytes, and lymphocytes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The prognostic value of SIRI likely reflects the complex interplay between tumor-promoting inflammation and anti-tumor immunity within the cancer microenvironment.\u003c/p\u003e \u003cp\u003eNeutrophils and monocytes represent components of the innate immune system that can promote cancer progression through multiple mechanisms. Tumor-associated neutrophils can facilitate angiogenesis, promote metastasis through the release of neutrophil extracellular traps, and create an immunosuppressive microenvironment that impairs T-cell function [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, tumor-associated macrophages, primarily derived from circulating monocytes, can adopt an alternatively activated (M2) phenotype that promotes tumor growth, angiogenesis, and immune suppression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Conversely, lymphocytes, particularly cytotoxic T-lymphocytes and natural killer cells, represent the adaptive immune response against malignancy and are generally associated with improved outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe incorporation of all three cell types in SIRI provides a more comprehensive assessment of the systemic immune-inflammatory balance compared to traditional two-component ratios. Recent studies in other malignancies have demonstrated the superiority of SIRI over NLR and PLR in predicting outcomes, supporting our findings in endometrial cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The threshold of 2.059 identified in our study through ROC analysis provides a clinically applicable cut-off for risk stratification in routine clinical practice.\u003c/p\u003e \u003cp\u003eThe GLR emerged as another independent predictor of poor survival (HR: 2.841, 95% CI: 1.206\u0026ndash;6.693, p\u0026thinsp;=\u0026thinsp;0.013), representing a novel marker that combines metabolic and immune components. This finding highlights the important intersection between metabolic dysfunction and immune status in cancer prognosis. Elevated glucose levels may indicate insulin resistance, metabolic syndrome, or diabetes, all of which have been associated with poor outcomes in endometrial cancer through multiple mechanisms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHyperglycemia creates a tumor-permissive environment through several pathways. Elevated glucose provides metabolic fuel for rapidly proliferating cancer cells, which preferentially utilize glucose through aerobic glycolysis (the Warburg effect) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, hyperglycemia can promote angiogenesis through advanced glycation end products and inflammatory pathways, facilitating tumor growth and metastasis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Insulin resistance, often accompanying hyperglycemia, leads to elevated insulin and insulin-like growth factor-1 levels, which can directly stimulate cancer cell proliferation and survival [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lymphocyte component of GLR reflects the host's adaptive immune response against the tumor. Lymphopenia in cancer patients often indicates immune suppression or exhaustion, which can result from tumor-derived immunosuppressive factors, chronic inflammation, or treatment-related effects [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The combination of elevated glucose and decreased lymphocytes captured by GLR therefore represents a dual mechanism of cancer progression: metabolic support for tumor growth coupled with impaired immune surveillance.\u003c/p\u003e \u003cp\u003eOur finding that GLR\u0026thinsp;\u0026ge;\u0026thinsp;58.88 identifies high-risk patients suggests that interventions targeting both metabolic and immune components might be beneficial. Metformin, for example, has shown promise in endometrial cancer through both metabolic effects (improving insulin sensitivity and glucose metabolism) and direct anti-tumor properties [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, lifestyle interventions focusing on weight loss and glycemic control might improve outcomes in high-risk patients identified by elevated GLR.\u003c/p\u003e \u003cp\u003eOur findings are consistent with the growing body of literature demonstrating the prognostic significance of inflammatory markers in endometrial cancer. Nishio et al., in their large multicenter Japanese study of 712 patients, found that NLR, PLR, and HALP scores were prognostic factors for both progression-free survival and overall survival [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, their study included all histological subtypes and did not account for benign uterine conditions that might confound inflammatory marker interpretation.\u003c/p\u003e \u003cp\u003eMa et al. demonstrated that elevated NLR, PLR, and MLR predicted poor outcomes in 156 advanced endometrial cancer patients receiving radiotherapy, with their nomogram achieving high predictive accuracy (C-index: 0.995) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Yanazume et al. found that NLR was an independent prognostic factor for both progression-free survival and overall survival in advanced endometrial cancer patients receiving immunotherapy, with optimal cut-off values of 4.92 and 5.40 respectively [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe meta-analysis by Leng et al., encompassing 14 studies with 5,274 patients, confirmed that pretreatment NLR and PLR were biomarkers of poor prognosis in endometrial cancer, with pooled hazard ratios of 2.51 for NLR and 2.50 for PLR regarding overall survival [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings support the clinical utility of inflammatory markers as prognostic tools in endometrial cancer management.\u003c/p\u003e \u003cp\u003eOur study extends these findings by introducing novel markers like SIRI and GLR while addressing the important issue of confounding benign conditions. The exclusion of patients with adenomyosis and myoma uteri represents a methodological advancement that enhances the specificity of inflammatory marker assessment for cancer-related processes. This approach is particularly relevant given that adenomyosis affects up to 30% of women with endometrial cancer and can significantly elevate inflammatory markers through chronic inflammatory processes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prognostic value of inflammatory markers in endometrial cancer reflects several interconnected biological mechanisms. Cancer-related inflammation promotes tumor progression through multiple pathways including DNA damage from reactive oxygen species, stimulation of angiogenesis through inflammatory mediators, and activation of oncogenic signaling pathways such as NF-κB [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Systemic inflammation also contributes to cancer cachexia and reduces the effectiveness of anti-cancer treatments by impairing immune function and promoting treatment resistance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe identification of high-risk patients through inflammatory markers has important therapeutic implications. Patients with elevated SIRI or GLR might benefit from more intensive surveillance protocols, consideration of adjuvant therapy even in early-stage disease, and enrollment in clinical trials investigating novel therapeutic approaches. Additionally, interventions targeting inflammation, such as aspirin or other anti-inflammatory agents, might improve outcomes in high-risk patients, though this requires prospective validation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe metabolic component captured by GLR suggests that interventions targeting glucose metabolism and insulin resistance might be particularly beneficial in high-risk patients. Beyond metformin, other diabetes medications such as GLP-1 receptor agonists have shown anti-cancer properties in preclinical studies and might warrant investigation in endometrial cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe inflammatory markers identified in our study could be readily implemented in clinical practice as they are derived from routine blood tests available in all healthcare settings. The calculation of SIRI and GLR is straightforward and could be automated in laboratory information systems or incorporated into electronic health records to provide real-time risk assessment.\u003c/p\u003e \u003cp\u003eThese markers could enhance current risk stratification systems by providing additional prognostic information beyond traditional histopathological factors. Patients identified as high-risk by inflammatory markers might benefit from more aggressive treatment approaches, including extended surgical staging, adjuvant therapy in earlier-stage disease, or enrollment in clinical trials investigating novel treatments. Conversely, low-risk patients might be candidates for de-escalated treatment approaches, reducing treatment-related morbidity while maintaining oncologic outcomes.\u003c/p\u003e \u003cp\u003eThe integration of inflammatory markers with molecular classification represents an exciting future direction. Recent advances in endometrial cancer classification based on molecular features (POLE mutations, microsatellite instability, p53 abnormalities) provide additional prognostic and predictive information [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Combining inflammatory markers with molecular features might create even more refined risk stratification tools for personalized treatment selection.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged in our study. The retrospective design introduces inherent selection bias and limits the ability to control for all potential confounding factors. The relatively small sample size of 142 patients, while adequate for initial validation, requires confirmation in larger multicenter cohorts. The single-center design may limit generalizability to other populations with different demographic characteristics, treatment patterns, or healthcare systems.\u003c/p\u003e \u003cp\u003eThe relatively short median follow-up of 28 months, while appropriate for initial survival analysis, limits assessment of long-term outcomes and late recurrences. Longer follow-up will be necessary to fully establish the prognostic value of these markers for disease-specific survival and to assess their utility in predicting late treatment effects or secondary malignancies.\u003c/p\u003e \u003cp\u003eMissing data for some inflammatory markers, particularly C-reactive protein and tumor markers, prevented comprehensive analysis of all potentially relevant biomarkers. Future studies should include systematic collection of additional inflammatory and metabolic markers to create more comprehensive prognostic models.\u003c/p\u003e \u003cp\u003eThe lack of information about adjuvant treatment decisions and adherence represents another limitation, as these factors could influence survival outcomes independently of inflammatory markers. Future studies should systematically collect treatment data to assess whether inflammatory markers predict treatment response or guide treatment selection.\u003c/p\u003e \u003cp\u003eFuture research should focus on several key areas to advance the clinical utility of inflammatory markers in endometrial cancer. Large-scale multicenter prospective studies are needed to validate our findings and establish standardized cut-off values applicable across different populations and laboratory systems. These studies should include diverse patient populations to ensure generalizability and should systematically collect comprehensive clinical, pathological, and treatment data.\u003c/p\u003e \u003cp\u003eLongitudinal assessment of inflammatory marker changes during treatment represents an important research direction. Serial measurements of SIRI, GLR, and other markers during chemotherapy, radiation therapy, or immunotherapy might provide dynamic prognostic information and help guide treatment modifications. Understanding how these markers change in response to successful treatment versus disease progression could enhance their clinical utility.\u003c/p\u003e \u003cp\u003eInvestigation of inflammatory markers in other endometrial cancer subtypes, including serous, clear cell, and carcinosarcoma variants, is needed to determine whether the prognostic value extends beyond endometrioid cancers. These aggressive subtypes often have different biological characteristics and treatment responses, and inflammatory markers might provide different prognostic information.\u003c/p\u003e \u003cp\u003eThe integration of inflammatory markers with molecular classification and other novel biomarkers represents a promising future direction. Combining traditional histopathological factors, molecular features, and inflammatory markers might create comprehensive prognostic models that better predict individual patient outcomes and guide personalized treatment selection.\u003c/p\u003e \u003cp\u003eDevelopment of targeted interventions based on inflammatory marker status should be a priority for future clinical trials. Patients with elevated inflammatory markers might benefit from anti-inflammatory treatments, metabolic interventions, or immunomodulatory therapies. Prospective trials testing these interventions in biomarker-selected populations could demonstrate the therapeutic utility of inflammatory marker assessment.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that preoperative inflammatory markers, particularly the systemic inflammatory response index (SIRI) and glucose-to-lymphocyte ratio (GLR), serve as independent prognostic factors in patients with endometrioid-type endometrial cancer. By systematically excluding patients with adenomyosis and myoma uteri, we provide evidence that these markers reflect cancer-specific inflammatory processes rather than benign inflammatory conditions. The SIRI\u0026thinsp;\u0026ge;\u0026thinsp;2.059 and GLR\u0026thinsp;\u0026ge;\u0026thinsp;58.88 thresholds identify high-risk patients who might benefit from intensified surveillance, consideration of adjuvant therapy, or enrollment in clinical trials investigating novel therapeutic approaches.\u003c/p\u003e \u003cp\u003eThese readily available, cost-effective biomarkers derived from routine blood tests could enhance current risk stratification systems by providing additional prognostic information beyond traditional histopathological factors. The combination of inflammatory, metabolic, and immune components captured by these markers offers insights into the complex biological processes underlying endometrial cancer progression and suggests potential therapeutic targets for improving patient outcomes.\u003c/p\u003e \u003cp\u003eHowever, validation in larger, prospective, multicenter studies is essential before clinical implementation. Future research should focus on establishing standardized cut-off values, investigating the utility of these markers in other endometrial cancer subtypes, and developing targeted interventions for high-risk patients identified by inflammatory marker assessment. The findings support the growing recognition that systemic inflammation plays a crucial role in cancer progression and that simple blood-based markers can provide valuable prognostic information complementing traditional pathological factors in the era of personalized cancer medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\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 2024/532 dated 05/06/2024).\u003c/p\u003e \u003ch2\u003eApproval for publication\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCode usability\u003c/strong\u003e \u003cp\u003eCan be used\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGU, KA and HY developed the concept and were responsible for data collection. SGG, GU and PA planned the study. GU, KA and HY analysed the results. GU, TTI and PA 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\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConcin N, Matias-Guiu X, Vergote I, Cibula D, Mirza MR, Marnitz S, et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer. 2021;31:12\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiakos CI, Charles KA, McMillan DC, Clarke SJ. 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Insulin resistance: a significant risk factor of endometrial cancer. Gynecol Oncol. 2012;125:751\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarburg O. On the origin of cancer cells. Science (1979). 1956;123:309\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyu TY, Park J, Scherer PE. Hyperglycemia as a risk factor for cancer progression. Diabetes Metab J. 2014;38:330.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallagher EJ, LeRoith D. Obesity and diabetes: the increased risk of cancer and cancer-related mortality. Physiol Rev. 2015;95:727\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLissoni P, Brivio F, Fumagalli L, Messina G, Ghezzi V, Frontini L, et al. Efficacy of cancer chemotherapy in relation to the pretreatment number of lymphocytes in patients with metastatic solid tumors. Int J Biol Markers. 2004;19:135\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardel M, Jensen SM, Potteg\u0026aring;rd A, J\u0026oslash;rgensen TL, Hallas J. Long-term use of metformin and colorectal cancer risk in type II diabetics: a population‐based case\u0026ndash;control study. Cancer Med. 2014;3:1458\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishio S, Murotani K, Yamagami W, Suzuki S, Nakai H, Kato K, et al. Pretreatment systemic inflammatory markers predict survival in endometrial cancer: A Japanese Gynecologic Oncology Group 2043 exploratory data analysis. Gynecol Oncol. 2024;181:46\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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;15:966.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYanazume S, Nagata C, Kobayashi Y, Fukuda M, Mizuno M, Togami S, et al. Potential prognostic predictors of treatment with immune checkpoint inhibitors for advanced endometrial cancer. Jpn J Clin Oncol. 2025;55:29\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeng J, Wu F, Zhang L. Prognostic Significance of Pretreatment Neutrophil-to-Lymphocyte Ratio, Platelet\u0026thinsp;\u0026ndash;\u0026thinsp;to \u0026ndash;\u0026thinsp;Lymphocyte Ratio, or Monocyte-to-Lymphocyte Ratio in Endometrial Neoplasms: A Systematic Review and Meta\u0026thinsp;\u0026ndash;\u0026thinsp;analysis. Front Oncol. 2022;12:734948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaffone A, Seracchioli R, Raimondo D, Maletta M, Travaglino A, Raimondo I, et al. Prevalence of adenomyosis in endometrial cancer patients: a systematic review and meta-analysis. Arch Gynecol Obstet. 2021;303:47\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454:436\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArgil\u0026eacute;s JM, Busquets S, Stemmler B, L\u0026oacute;pez-Soriano FJ. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer. 2014;14:754\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRothwell PM, Wilson M, Elwin C-E, Norrving B, Algra A, Warlow CP, et al. Long-term effect of aspirin on colorectal cancer incidence and mortality: 20-year follow-up of five randomised trials. Lancet. 2010;376:1741\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourou MZ, Matsas A, Valsamakis G, Vlahos N, Panoskaltsis T, BOUROU MZOI et al. The potential role of glucagon-like peptide-1 (GLP-1) receptor agonists as a type of conservative treatment of endometrial cancer in women of reproductive age: a review of the literature and a call for study. Cureus. 2024;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe\u0026oacute;n-Castillo A, De Boer SM, Powell ME, Mileshkin LR, Mackay HJ, Leary A, et al. Molecular classification of the PORTEC-3 trial for high-risk endometrial cancer: impact on prognosis and benefit from adjuvant therapy. J Clin Oncol. 2020;38:3388\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"endometrial cancer, inflammatory markers, prognosis, neutrophil-lymphocyte ratio, systemic inflammatory response index","lastPublishedDoi":"10.21203/rs.3.rs-8414305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8414305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSystemic inflammatory markers have emerged as prognostic indicators in various malignancies. This study investigates the relationship between preoperative inflammatory markers and survival outcomes in patients with endometrioid-type endometrial cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed 142 patients with endometrioid-type endometrial cancer who underwent surgical treatment between 2018 and 2024. Patients with coexistent adenomyosis and myoma uteri were excluded to eliminate confounding inflammatory effects. Preoperative inflammatory markers including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), pan-immune-inflammation value (PIV), and other hematological parameters were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median follow-up period was 28.0 months (range: 1\u0026ndash;51 months, IQR: 16\u0026ndash;37 months). Overall survival rate was 44.344\u0026thinsp;\u0026plusmn;\u0026thinsp;1.278 months. Cumulative survival rates were 96% at 12 months, 88% at 24 months, 82% at 36 months, and 72% at 48 months. Several inflammatory markers showed significant prognostic value: DNI\u0026thinsp;\u0026ge;\u0026thinsp;0.005763 (HR: 2.485, 95% CI: 1.011\u0026ndash;5.644), SIRI\u0026thinsp;\u0026ge;\u0026thinsp;2.059 (HR: 2.485, 95% CI: 1.042\u0026ndash;5.923), and GLR\u0026thinsp;\u0026ge;\u0026thinsp;58.88 (HR: 2.841, 95% CI: 1.206\u0026ndash;6.693).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative inflammatory markers, particularly SIRI and GLR, demonstrate significant prognostic value in endometrioid-type endometrial cancer. These readily available biomarkers may enhance risk stratification and guide personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Systemic Inflammatory Markers in Patients with Endometrioid-Type Endometrial Cancer: A Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 11:11:18","doi":"10.21203/rs.3.rs-8414305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"91990995240000710058450068538088808777","date":"2026-01-31T02:44:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T20:22:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12336652915521705833102012427007658898","date":"2026-01-24T20:17:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T01:54:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269517916262204529602716713426794779851","date":"2026-01-24T00:35:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T16:00:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T09:37:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-02T17:27:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T15:24:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-02T15:18:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bdf88b89-7bfd-4b24-8791-93498aaa87b8","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T11:11:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 11:11:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8414305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8414305","identity":"rs-8414305","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}