Analysis of Lymph Node Metastasis and Risk Factors in 424 Patients with low-grade endometrioid carcinoma
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Abstract Purpose: To explore the lymph node metastasis (LNM) of low-grade endometrioid carcinoma (EEC) and its related risk factors, and to analyze the efficacy of related risk factors in predicting LNM. Methods: Data of 424 EEC patients who underwent endometrial cancer staging surgery from January 2019 to June 2024 were retrospectively analyzed. Univariate and multivariate logistic regression were used to analyze the related factors of LNM. The receiver operating characteristic (ROC) curve was drawn to analyze the efficacy of related independent risk factors in predicting LNM. Results: The rate of LNM was 7.8% (33/424). Univariate analysis showed that histological grade, tumor size, depth of myometrial, cervical stromal, lymphovascular space invasion (LVSI), microcystic, elongated, fragmented (MELF), carbohydrate antigen 125 (CA125), carbohydrate antigen 199 (CA199)and human epididymis protein 4 (HE4) were associated with LNM of EEC. Logistic regression analysis showed that LVSI, MELF, depth of myometria and CA125were independent risk factors for LNM. The ROC curve area of CA125 and depth of myometria was 0.796 and 0.734. The best cut-off point of CA125 was 31.36 (U/mL), corresponding to the largest Youden index of 53.9%, and its positive likelihood ratio was 5.2. The accuracy of diagnosing LNM by combining CA125 and depth of myometrial is higher than that of either alone. Conclusions: LNM is more likely to occur when there are risk factors such as LVSI positive, MELF invasion pattern, myometrial invasion depth ≥50% and CA125. The accuracy of CA125 combined with depth of myometrial in the diagnosis of LNM is higher than that of either alone. This study has a certain reference value for predicting the risk of LNM and stratified management and treatment for EEC patients.
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Methods: Data of 424 EEC patients who underwent endometrial cancer staging surgery from January 2019 to June 2024 were retrospectively analyzed. Univariate and multivariate logistic regression were used to analyze the related factors of LNM. The receiver operating characteristic (ROC) curve was drawn to analyze the efficacy of related independent risk factors in predicting LNM. Results: The rate of LNM was 7.8% (33/424). Univariate analysis showed that histological grade, tumor size, depth of myometrial, cervical stromal, lymphovascular space invasion (LVSI), microcystic, elongated, fragmented (MELF), carbohydrate antigen 125 (CA125), carbohydrate antigen 199 (CA199)and human epididymis protein 4 (HE4) were associated with LNM of EEC. Logistic regression analysis showed that LVSI, MELF, depth of myometria and CA125were independent risk factors for LNM. The ROC curve area of CA125 and depth of myometria was 0.796 and 0.734. The best cut-off point of CA125 was 31.36 (U/mL), corresponding to the largest Youden index of 53.9%, and its positive likelihood ratio was 5.2. The accuracy of diagnosing LNM by combining CA125 and depth of myometrial is higher than that of either alone. Conclusions: LNM is more likely to occur when there are risk factors such as LVSI positive, MELF invasion pattern, myometrial invasion depth ≥50% and CA125. The accuracy of CA125 combined with depth of myometrial in the diagnosis of LNM is higher than that of either alone. This study has a certain reference value for predicting the risk of LNM and stratified management and treatment for EEC patients. Low-grade endometrioid carcinoma(EEC) Lymph node metastasis(LNM) Risk factors Predicting Figures Figure 1 Introduction Endometrial cancer (EC) is a common malignant tumor in gynecology. Globally, EC ranks seventh among all female cancers and is more common in middle-aged and elderly women [ 1 ]. In recent years, the differentiation of EC is divided into low grade (grade 1 and grade 2) and high grade (grade 3), which has been recommended by the International Association of Gynecological Pathologists [ 2 ] and the World Health Organization(WHO)Classification of Tumors of Female Reproduction 2020 [ 3 ]. Lymph node metastasis (LNM) is the main prognostic factor and the main metastasis mode of EC. LNM still occurs of 10% even in low-risk endometrioid carcinoma patients [ 4 ]. At present, hysterectomy + bilateral salpingo-oophorectomy + pelvic lymph node and (or) para-aortic lymph node resection is still the routine operation for staging and treatment of EC[ 5 ]. According to the opinions of the National Cancer Network (NCCN), lymph node evaluation is recommended for EC patients, but there are still differences and controversies on the scope and mode of lymph node resection [ 6 ]. Big data analysis shows that the overall survival time of early EC is not significantly prolonged by lymph node resection [ 7 ]. In clinical work, it is necessary to correctly predict the risk of LNM. If the risk factors of LNM can be determined and the preoperative risk stratification can be performed, overtreatment may be avoided, but there is still no consensus [ 8 , 9 ]. Therefore, more detailed risk stratification of LNM in low-risk patients is still the direction of most research efforts [ 9 – 11 ], and further research with a larger number of cases is of great clinical significance. In our study, the medical records of 424 patients with low-grade endometrioid carcinoma (EEC)in our hospital were retrospectively analyzed to provide reference value for the risk factors and stratified management of EEC patients. Materials and methods The clinical data of patients with EEC who underwent surgery in the Department of Gynecology, the Affiliated Hospital of Xuzhou Medical University from January 2019 to June 2024 were retrospectively analyzed. Surgical treatment included hysterectomy, bilateral salpingo-oophorectomy, pelvic lymph node and para-aortic lymph node resection. Inclusion criteria: 1, EEC patients, grade 1 and grade 2 (G1-G2), confirmed by pathology. 2, patients underwent staging surgery for EC in our hospital with complete clinical data. 3, Complete data of peripheral blood data (preoperative blood routine and tumor markers): carbohydrate antigen 125 (CA125), carbohydrate antigen 199 (CA199), carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), human epididymis protein 4 (HE4). Exclusion criteria: 1, special pathological type, grade 3, secondary malignant tumor, the presence of other synchronous malignant tumors; 2, received preoperative adjuvant therapy (such as radiotherapy, chemotherapy or hormone therapy); 3, complicated with infectious diseases; 4, EEC patients did not undergo lymphadenectomy, and those with incomplete medical records were also excluded. Patients’ data were collected by reviewing medical records, operation records, preoperative laboratory results and postoperative pathological reports. The clinicopathological characteristics of the patients were recorded, including age at surgery (years), body mass index (BMI, kg/m2), reproductive history, menopause, tubal ligation, hypertension, diabetes, uterine leiomyoma, histological grade (G1 or G2), International Federation of Gynecology and Obstetrics (FIGO) 2009 stage, tumor size (< 2cm or ≥ 2cm), depth of myometrial invasion (< 50% or ≥ 50%), serous layer/adnexa invasion, cervical stromal invasion, lymphovascular space invasion (LVSI), microcystic, elongated, fragmented (MELF) invasion pattern, and LNM. The following preoperative laboratory results were also collected: levels of serum CA125 (U/mL),CA199 (U/mL),CEA (ng/mL),AFP (ng/mL),HE4 (pmol/L), absolute neutrophil count (ANC, 10 9 /L), absolute lymphocyte count (ALC, 10 9 /L), absolute monocyte count (AMC, 10 9 /L), and platelet count (PLT, 10 9 /L). Moreover, neutrophil to lymphocyte ratio (NLR, =ANC/ALC), platelet to lymphocyte ratio (PLR, =PLT/ALC) and monocyte to lymphocyte ratio (MLR, =AMC/ALC) were calculated and recorded. Surgical pathological staging was performed using the FIGO 2009 criteria [ 12 ]. Statistical analysis We performed statistical analyses using the SPSS 23.0 software package (IBM Corp., Armonk, NY, USA). In univariate analysis, the Mann–Whitney U test for continuous variables (because they’re not normally distributed) and the Chi-square or Fisher’s exact tests for categorical variables were used. Variables with P value < 0.05 in univariate analysis were included in multivariate analysis. Multivariable logistic regression analysis was used to identify independent risk factors. The receiver operating characteristic curve (ROC) was drawn to determine the ability of related independent risk factors to predict LNM in EEC patients, and determine the optimal cut-of value of risk factors. Results General situation A total of 424 subjects met the criteria and were included in the study. According to the FIGO 2009 criteria, there were 305 cases (71.9%) in FIGO stage ⅠA, 51 cases (12.0%) in stage ⅠB, and 21 cases (5.0%) in stage Ⅱ. There were 45 cases (10.6%) in FIGO stage Ⅲ, which included 12 cases (2.8%) in stage ⅢA, 33 cases (7.8%) in stage IIIC. Even 2 cases (0.5%) in FIGO stage IV (Note: These two patients had no LNM, but both had fallopian tube metastasis and omentum metastasis). According to the final pathological results, the patients were divided into lymph node metastasis group (positive group) and lymph node non-metastasis group (negative group). There were 33 cases (33/424, 7.8%) in the positive group and 391 cases (391/424, 92.2%) in the negative group. The median age of the positive group was 56 years (age range 38–79 years), and the median age of the negative group was 55 years (age range 24–84 years). The median number of lymph nodes removed in the positive group was 10 (5–23) in the left pelvic cavity, 10 (5–28) in the right pelvic cavity, and 5 (1–27) in the para-aortic region. In the negative group, the median number of lymph nodes removed was 9 (1–34) in the left pelvic cavity, 8 (1–27) in the right pelvic cavity, and 5 (1–20) in the para-aortic region. There was no significant difference between the two groups ( P > 0.05). Among the 33 patients with LNM, the frequency and number of LNM: left pelvis lymph nodes region appeared 20 times (1–4), the right pelvis lymph nodes region appeared 19 times (1–9), and 13 times (1–7) were found in the para-aortic region. In addition, there were 3 patients with isolated para-aortic lymph node metastasis (no pelvic lymph node metastasis). The clinical and pathological variables of the 424 EEC patients are shown in Table 1 , and the peripheral blood data are shown in Table 2 . Table 1 Clinical and pathological characteristics of 424 EEC patients Variable No. of patients % Age (years) ≥ 60 117 27.6 <60 307 72.4 BMI (kg/m2) ≥ 25 248 58.49 <25 176 41.51 Menopause Yes 326 76.89 No 98 23.11 Tubal ligation Yes 41 9.7 No 383 90.3 Live birth Yes 413 97.41 No 11 2.59 Hypertension Yes 166 39.2 No 258 60.8 Diabetes Yes 89 21.0 No 335 79.0 Uterine leiomyoma Yes 198 46.7 No 226 53.3 Histologic grading G2 181 42.7 G1 243 57.3 LVSI Positive 35 8.3 Negative 389 91.7 MELF Yes 16 3.8 No 408 96.2 Tumor size ≥ 2cm 173 40.8 <2cm 251 59.2 Myometrial invasion ≥ 50% 87 20.5 <50% 337 79.5 Cervical stromal Yes 31 7.3 No 393 92.7 Serous layer/Adnexa Yes 16 3.8 No 408 96.2 LNM Yes 33 7.8 No 391 92.2 BMI, body mass index; LVSI, lymphovascular space invasion; MELF, microcystic, elongated, fragmented; LNM, Lymph node metastasis. Table 2 Peripheral blood data of 424 EEC patients Factors Scope M (P25-P75) MLR 0.04-1.00 0.18(0.14–0.23) PLR 42.86-581.67 148.50(117.16-193.05) NLR 0.59–21.26 2.07(1.57–2.79) CA125 3.57–535.00 15.64(11.60–25.10) CA199 0.60-547.80 13.30(8.21–24.04) CEA 0.20–36.70 1.70(1.19–2.58) AFP 0.88–9.98 3.02(2.10–3.92) HE4 25.94–1242.00 60.75(49.21–79.08) NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; MLR, monocyte to lymphocyte ratio. Note: Did not conform to the normal distribution and were expressed as M (P25-P75). Univariate analysis of clinicopathological parameters Univariate analysis showed that histological grade, tumor size, depth of myometrial invasion, cervical stromal invasion, LVSI and MELF invasion pattern were associated with LNM in EEC patients( P < 0. 05). (Table 3 ) Table 3 Characteristics of 424 EEC patients according to LNM Risk factors LNM, n (%) χ2 Ρ value positive ( n = 33) negative ( n = 391) Age (years) ≥ 60 <60 12(10.3) 21(6.8) 105(89.7) 286(93.2) 1.377 0.241 BMI (kg/m 2 ) ≥ 25 <25 14(5.6) 19(10.8) 234(94.4) 157(89.2) 3.805 0.051 Menopause Yes No 26(8.0) 7(7.1) 300(92.0) 91(92.9) 0.073 0.787 Tubal ligation Yes No 4(9.8) 29(7.6) 37(90.2) 354(92.4) 0.036 0.850 Live birth Yes No 31(7.5) 2 (18.2) 382(92.5) 9(81.8) 0.539 0.463 Hypertension Yes No 9(5.4) 24(9.3) 157(94.6) 234(90.7) 2.119 0.145 Diabetes Yes No 8(9.0) 25(7.5) 81(91.0) 310(92.5) 0.228 0.633 Uterine leiomyoma Yes No 16(8.1) 17(7.5) 182(91.9) 209(92.5) 0.046 0.830 Histologic grading G2 G1 21(11.6) 12(4.9) 160(88.4) 231(95.1) 6.418 0.011 LVSI Positive negative 19(54.3) 14(3.6) 16(45.7) 375(96.4) 107.989 <0.001 MELF Yes No 13(81.2) 20(4.9) 3(18.8) 388(95.1) 114.629 <0.001 Tumor size ≥ 2cm <2cm 21(12.1) 12(4.8) 152(87.9) 239(95.2) 7.725 0.005 Myometrial invasion ≥ 50% <50% 21(24.1) 12(3.6) 66(75.9) 325(96.4) 40.794 <0.001 Cervical stromal Yes No 6(19.4) 27(6.9) 25(80.6) 366(93.1) 4.622 0.032 Serous layer/Adnexa Yes No 3(18.8) 30(7.4) 13(81.2) 378(92.6) 1.425 0.233 BMI, body mass index; LVSI, lymphovascular space invasion; MELF, microcystic, elongated, fragmented; LNM, Lymph node metastasis. Univariate analysis of some indicators of peripheral blood Univariate analysis of preoperative peripheral blood indicators showed : CA125, CA199 and HE4 were associated with LNM ( P < 0. 05), while NLR, PLR, MLR, CEA and AFP were not associated with LNM. (Table 4 ) Table 4 Comparison of preoperative peripheral blood indexes in EEC patients according to LNM [Median(P25-P75) ] positive negative Z Ρ value NLR 1.82(1.56–2.64) 2.08(1.57–2.80) -0.831 0.406 PLR 151.82(100.95–196.00) 146.47(117.36-190.96) -0.382 0.702 MLR 0.17(0.13–0.22) 0.18(0.14–0.23) -0.360 0.719 CA125 37.59(17.62–66.68) 15.23(11.32–23.70) 5.646 <0.001 CA199 24.38(8.71–93.20) 13.20(8.20-22.77) 2.535 0.011 CEA 1.94(1.31–2.96) 1.70(1.18–2.54) 1.136 0.256 AFP 3.03(1.83–3.36) 3.00(2.11–3.97) -0.953 0.341 HE4 69.60(57.91–91.19) 60.20(48.45–76.81) 2.560 0.010 NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; MLR, monocyte to lymphocyte ratio. Multivariate logistic regression analysis of risk factors for LNM in EEC patients Logistic regression analysis showed that LVSI, MELF invasion pattern, myometrial invasion depth and CA125 were independent risk factors for LNM ( OR > 1, P < 0. 05). (Table 5 ) Table 5 Logistic regression analysis of risk factors for LNM Factors β SE Wald P OR OR 95% CI Lower Upper LVSI 2.798 0.543 26.590 <0.001 16.415 5.667 47.549 MELF 3.110 0.839 13.741 <0.001 22.428 4.331 116.151 Myometrial invasion 1.380 0.518 7.089 0.008 3.973 1.439 10.969 CA125 0.014 0.005 9.014 0.003 1.014 1.005 1.023 LVSI, lymphovascular space invasion; MELF, microcystic, elongated, fragmented. The efficacy of diagnosing LNM by combining CA125 and depth of myometrial invasion or either alone The area under the ROC curve of CA125 and depth of myometrial invasion were 0.796 and 0.734 respectively, which were statistically significant( P <0.05). The optimal diagnostic cut-off point of CA125 was 31.36 (U/mL), corresponding to the largest Youden index of 53.9%, and its positive likelihood ratio was 5.2. Among the four independent risk factors, the ROC curve was used to analyze the predictive efficacy of CA125 and myometrial invasion depth alone or in combination for LNM. According to the area under the ROC curve, the accuracy of diagnosing LNM by combining CA125 and depth of myometrial invasion is higher than that of either alone. (Table 6 , Fig. 1 ) Table 6 Area under the ROC curve Variables Area P 95% CI Lower Upper CA125 0.796 <0.001 0.716 0.876 Myometrial invasion 0.734 <0.001 0.634 0.833 Predicted probability 0.840 <0.001 0.765 0.914 Discussion A total of 424 patients with EEC were included in our study, with a LNM rate of 7.8%. Among them, 305 cases (71.9%) were at FIGO 2009 stage IA and 51 cases (12.0%) at stage IB, accounting for 83.9% (356/424) of stage I patients. CA125, myometrial invasion depth, LVSI and MELF pattern were independent risk factors for LNM in EEC patients. The optimal diagnostic cut-off point of preoperative CA125 was 31.36 (U/mL). Among the four independent risk factors, only myometrial invasion depth and CA125 could be evaluated preoperatively by imaging examination (magnetic resonance imaging, MRI) and serological examination. Therefore, by comparing the area under the ROC curve, we found that the combination of CA125 and depth of myometrial invasion was more accurate in diagnosing LNM than either alone. Our result further support a study in Korea [ 11 ], all confirm that MRI and CA125 are very important in the preoperative evaluation of endometrial cancer [ 13 – 14 ]. In conclusion, low risk EEC also has the possibility of LNM, if the preoperative examination shows deep myometrial invasion and high CA125, it is necessary to pay attention to the possibility of LNM, and systematic lymph node resection is recommended. So we should pay attention to the evaluation of CA125 and myometrial invasion before operation, and we should pay attention to the identification of risk factors and do a more detailed hierarchical management for EEC patients. LVSI positivity has long been considered to predict a poor prognosis, which was also confirmed in our study. Studies have shown that LVSI is a predictor of LNM in endometrial cancer and an important factor affecting the prognosis of patients with endometrial cancer [ 15 – 17 ]. A large Swedish study showed [ 18 ] that LVSI was the strongest independent risk factor for lymph node metastasis and survival in patients with endometrioid adenocarcinoma. In a study of 25,907 patients, Jorge et al. [ 19 ] confirmed that LVSI was independently associated with lymph node metastasis and explored its risk degree(LVSI was associated with increased risks of LN metastasis by a magnitude of 3 to over 10-fold). However, at present, we cannot know whether LVSI is positive before surgery, and it would be of interest to investigate whether a reliable risk of lymph node involvement can be determined based on biopsy results. For some patients without endometrial cancer staging surgery, if LVSI is found to be positive, it may indicate the need for lymph nodes resection or adjuvant therapy [ 20 ]. MELF invasion was found in 16 cases, and 13 cases (13/16,81.2%) had LNM. Para-aortic lymph node metastasis occurred in 6 patients. It is suggested that MELF infiltration pattern is prone to LNM, even para-aortic lymph node metastasis. Studies have shown that MELF invasion is a unique invasion pattern of endometrial carcinoma, which is limited to low-grade invasive carcinoma of endometrioid type [ 21 – 23 ]. MELF invasion is a risk factor for LNM in endometrial cancer [ 24 – 25 ]. Lymph nodes from grade I EEC high-grade cellular budding or LVI should be examined for occult metastases, especially in the form of histiocyta-like cells[ 22 ]. By analyzing the location of positive lymph nodes in patients with LNM, it was found that 3 patients had no pelvic lymph node metastasis, only para-aortic lymph node metastasis. The clinical data of the three patients showed diffusely positive LVSI or MELF pattern invasion. It can be concluded that LVSI positive and MELF are both independent risk factors for lymph node metastasis, and are also more likely to have isolated para-aortic lymph node metastasis, which is worthy of further study with a larger sample size. Furthermore, some studies have found that grade 2 lesions should be regarded as an important prognostic factor for the recurrence of low-risk endometrial cancer [ 26 ]. Also, it has been pointed out that tumor diameter is related to LNM and recurrence in endometrial cancer patients [ 27 ], and tumor size is an independent predictor of LNM and survival in early-stage endometrioid endometrial cancer [ 28 ]. However, our research results show that histological grade and tumor size are related to lymph node metastasis in EEC, but they are not independent risk factors. Some studies have indicated that pretreatment NLR and HE4 are predictors of lymph node metastasis in endometrial cancer patients [ 29 – 32 ]. Our study aimed to verify this result in EEC, but unfortunately, it was not verified. The statistical results of NLR, PLR, MLR, and HE4 were not independent risk factors for EEC. Limitations Over-treatment is one of the problems existing in the treatment methods for endometrial cancer [ 8 ]. Therefore, for EEC, we attempted to identify independent risk factors influencing LNM, in an effort to make some contributions to more detailed risk stratification. This study reminds us that preoperative MRI and other examinations are very important for the judgment of myometrial invasion depth, and the application of sentinel lymph nodes needs to be strengthened[ 33 – 34 ]. Although histological grade G2, tumor size ≥ 2 cm, cervical stromal invasion, CA199 and HE4 are not independent risk factors in EEC, they are also worthy of our continuous attention. It may also be necessary to continue to increase sample research in the future. Our study is a single-center and retrospective study, maybe another multicenter, large-sample, and prospective study is required to provide a more valuable reference for clinical practice. Conclusion LVSI, MELF invasion pattern, depth of myometrial invasion and CA125 are independent risk factors for LNM in EEC according to FIGO 2009. When these risk factors are present, LNM is more likely to occur. The accuracy of diagnosing LNM by combining CA125 and depth of myometrial invasion is higher than that of either alone. This study has certain reference value for predicting the risk of LNM and stratified management of treatment in patients with EEC. Declarations Statement of Ethics This retrospective study was approved and informed consent was waived by the institutional review board of the Affiliated Hospital of Xuzhou Medical University. Conflict of interest All authors have no conflict of interest. Funding No funding. Data Availability All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author. Author Contribution Lina Cao is the first author and corresponding author, who designed the study and wrote the manuscript. Xiaoyuan Lu supervised the manuscript and our study. Yijun Wang and Luyao Wang contributed to data collection. 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MELF Pattern for Predicting Lymph Node Involvement and Survival in Grade I-II Endometrioid-type Endometrial Cancer. Int J Gynecol Pathol. 2018;37(1):17-21. doi:10.1097/PGP.0000000000000370 Bak SE, Yoo JG, Lee SJ, Yoon JH, Park DC, Kim SI. Prognostic significance of histological grade in low-risk endometrial cancer. Int J Med Sci. 2022;19(13):1875-1878. Published 2022 Oct 24. doi:10.7150/ijms.77152 Fu R, Zhang D, Yu X, Zhang H. The association of tumor diameter with lymph node metastasis and recurrence in patients with endometrial cancer: a systematic review and meta-analysis. Transl Cancer Res. 2022;11(11):4159-4177. doi:10.21037/tcr-22-2595 Mahdi H, Munkarah AR, Ali-Fehmi R, Woessner J, Shah SN, Moslemi-Kebria M. Tumor size is an independent predictor of lymph node metastasis and survival in early stage endometrioid endometrial cancer. Arch Gynecol Obstet. 2015;292(1):183-190. doi:10.1007/s00404-014-3609-6 Aoyama T, Takano M, Miyamoto M, et al. Pretreatment Neutrophil-to-Lymphocyte Ratio Was a Predictor of Lymph Node Metastasis in Endometrial Cancer Patients. Oncology. 2019;96(5):259-267. doi:10.1159/000497184 Lei H, Xu S, Mao X, et al. Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer. J Inflamm Res. 2021;14:7131-7142. Published 2021 Dec 21. doi:10.2147/JIR.S345790 Gao M, Gao Y. Value of preoperative neutrophil-lymphocyte ratio and human epididymis protein 4 in predicting lymph node metastasis in endometrial cancer patients. J Obstet Gynaecol Res. 2021;47(2):515-520. doi:10.1111/jog.14542 Mais V, Fais ML, Peiretti M, et al. HE4 Tissue Expression as A Putative Prognostic Marker in Low-Risk/Low-Grade Endometrioid Endometrial Cancer: A Review. Curr Oncol. 2022;29(11):8540-8555. Published 2022 Nov 10. doi:10.3390/curroncol29110673 Rossi EC, Kowalski LD, Scalici J, et al. A comparison of sentinel lymph node biopsy to lymphadenectomy for endometrial cancer staging (FIRES trial): a multicentre, prospective, cohort study. Lancet Oncol. 2017;18(3):384-392. doi:10.1016/S1470-2045(17)30068-2 Kimmig R, Thangarajah F, Buderath P. Sentinel Lymph node detection in endometrial cancer - Anatomical and scientific facts. Best Pract Res Clin Obstet Gynaecol. 2024;94:102483. doi:10.1016/j.bpobgyn.2024.102483 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5763156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398935652,"identity":"71ffba72-f778-4721-8788-07583d9faad5","order_by":0,"name":"Lina Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3RMQrCMBSA4SeRdGnt+kTQK6QEpMexuEpRXDqIBAqeQuoVFKFzS8Cp4NrR6gUcHRxMurk0jg75oYFAP/qSAthsf9iA6HWJAISUzTPB8cREaEuYIg6dc6xCHggTaVemHt+dordLIihMxPHKJ7Aw9lOXs2GGs54gzb3uHGwwRzXYGiVd3VY5xg5QzhedxGWaREKSMxvmuO4Jl44MhL80OUhQZ9krW5jJtP3KUfYVET+SUJOT1Jd8QR6khrP4fsVrSLZRdpXqV26244mTNo8uoiPv763hdZvNZrOZ+wBhUDySe7qK6QAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lina","middleName":"","lastName":"Cao","suffix":""},{"id":398935653,"identity":"b1058db3-3a5d-4b50-a78f-84d30af27cbb","order_by":1,"name":"Xiaoyuan Lu","email":"","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyuan","middleName":"","lastName":"Lu","suffix":""},{"id":398935654,"identity":"6994c063-bef4-4719-b547-e2114c002e66","order_by":2,"name":"Yijun Wang","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Wang","suffix":""},{"id":398935655,"identity":"91a1c01f-9bf2-4c21-ad56-b2e294410bb5","order_by":3,"name":"Luyao Wang","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Luyao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-04 11:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5763156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5763156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73671809,"identity":"f4e9cb09-7ad9-4d67-9539-c605cae624c8","added_by":"auto","created_at":"2025-01-13 12:45:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51528,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of CA125 and depth of myometrial invasion alone and in combination to predict LNM\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5763156/v1/23bed940470fa66115a2d441.png"},{"id":73676492,"identity":"3d82d66a-abe3-43e1-b095-7bf5983f52c2","added_by":"auto","created_at":"2025-01-13 13:09:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":923383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5763156/v1/06270238-9d14-4194-a745-b22edf429d3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Lymph Node Metastasis and Risk Factors in 424 Patients with low-grade endometrioid carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is a common malignant tumor in gynecology. Globally, EC ranks seventh among all female cancers and is more common in middle-aged and elderly women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, the differentiation of EC is divided into low grade (grade 1 and grade 2) and high grade (grade 3), which has been recommended by the International Association of Gynecological Pathologists [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and the World Health Organization(WHO)Classification of Tumors of Female Reproduction 2020 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLymph node metastasis (LNM) is the main prognostic factor and the main metastasis mode of EC. LNM still occurs of 10% even in low-risk endometrioid carcinoma patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. At present, hysterectomy\u0026thinsp;+\u0026thinsp;bilateral salpingo-oophorectomy\u0026thinsp;+\u0026thinsp;pelvic lymph node and (or) para-aortic lymph node resection is still the routine operation for staging and treatment of EC[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to the opinions of the National Cancer Network (NCCN), lymph node evaluation is recommended for EC patients, but there are still differences and controversies on the scope and mode of lymph node resection [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Big data analysis shows that the overall survival time of early EC is not significantly prolonged by lymph node resection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In clinical work, it is necessary to correctly predict the risk of LNM. If the risk factors of LNM can be determined and the preoperative risk stratification can be performed, overtreatment may be avoided, but there is still no consensus [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, more detailed risk stratification of LNM in low-risk patients is still the direction of most research efforts [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and further research with a larger number of cases is of great clinical significance.\u003c/p\u003e \u003cp\u003eIn our study, the medical records of 424 patients with low-grade endometrioid carcinoma (EEC)in our hospital were retrospectively analyzed to provide reference value for the risk factors and stratified management of EEC patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe clinical data of patients with EEC who underwent surgery in the Department of Gynecology, the Affiliated Hospital of Xuzhou Medical University from January 2019 to June 2024 were retrospectively analyzed. Surgical treatment included hysterectomy, bilateral salpingo-oophorectomy, pelvic lymph node and para-aortic lymph node resection. Inclusion criteria: 1, EEC patients, grade 1 and grade 2 (G1-G2), confirmed by pathology. 2, patients underwent staging surgery for EC in our hospital with complete clinical data. 3, Complete data of peripheral blood data (preoperative blood routine and tumor markers): carbohydrate antigen 125 (CA125), carbohydrate antigen 199 (CA199), carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), human epididymis protein 4 (HE4). Exclusion criteria: 1, special pathological type, grade 3, secondary malignant tumor, the presence of other synchronous malignant tumors; 2, received preoperative adjuvant therapy (such as radiotherapy, chemotherapy or hormone therapy); 3, complicated with infectious diseases; 4, EEC patients did not undergo lymphadenectomy, and those with incomplete medical records were also excluded.\u003c/p\u003e \u003cp\u003ePatients\u0026rsquo; data were collected by reviewing medical records, operation records, preoperative laboratory results and postoperative pathological reports. The clinicopathological characteristics of the patients were recorded, including age at surgery (years), body mass index (BMI, kg/m2), reproductive history, menopause, tubal ligation, hypertension, diabetes, uterine leiomyoma, histological grade (G1 or G2), International Federation of Gynecology and Obstetrics (FIGO) 2009 stage, tumor size (\u0026lt;\u0026thinsp;2cm or \u0026ge;\u0026thinsp;2cm), depth of myometrial invasion (\u0026lt;\u0026thinsp;50% or \u0026ge;\u0026thinsp;50%), serous layer/adnexa invasion, cervical stromal invasion, lymphovascular space invasion (LVSI), microcystic, elongated, fragmented (MELF) invasion pattern, and LNM. The following preoperative laboratory results were also collected: levels of serum CA125 (U/mL),CA199 (U/mL),CEA (ng/mL),AFP (ng/mL),HE4 (pmol/L), absolute neutrophil count (ANC, 10\u003csup\u003e9\u003c/sup\u003e/L), absolute lymphocyte count (ALC, 10\u003csup\u003e9\u003c/sup\u003e/L), absolute monocyte count (AMC, 10\u003csup\u003e9\u003c/sup\u003e/L), and platelet count (PLT, 10\u003csup\u003e9\u003c/sup\u003e/L). Moreover, neutrophil to lymphocyte ratio (NLR, =ANC/ALC), platelet to lymphocyte ratio (PLR, =PLT/ALC) and monocyte to lymphocyte ratio (MLR, =AMC/ALC) were calculated and recorded. Surgical pathological staging was performed using the FIGO 2009 criteria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe performed statistical analyses using the SPSS 23.0 software package (IBM Corp., Armonk, NY, USA). In univariate analysis, the Mann\u0026ndash;Whitney U test for continuous variables (because they\u0026rsquo;re not normally distributed) and the Chi-square or Fisher\u0026rsquo;s exact tests for categorical variables were used. Variables with \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multivariate analysis. Multivariable logistic regression analysis was used to identify independent risk factors. The receiver operating characteristic curve (ROC) was drawn to determine the ability of related independent risk factors to predict LNM in EEC patients, and determine the optimal cut-of value of risk factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGeneral situation\u003c/h2\u003e \u003cp\u003eA total of 424 subjects met the criteria and were included in the study. According to the FIGO 2009 criteria, there were 305 cases (71.9%) in FIGO stage ⅠA, 51 cases (12.0%) in stage ⅠB, and 21 cases (5.0%) in stage Ⅱ. There were 45 cases (10.6%) in FIGO stage Ⅲ, which included 12 cases (2.8%) in stage ⅢA, 33 cases (7.8%) in stage IIIC. Even 2 cases (0.5%) in FIGO stage IV (Note: These two patients had no LNM, but both had fallopian tube metastasis and omentum metastasis).\u003c/p\u003e \u003cp\u003eAccording to the final pathological results, the patients were divided into lymph node metastasis group (positive group) and lymph node non-metastasis group (negative group). There were 33 cases (33/424, 7.8%) in the positive group and 391 cases (391/424, 92.2%) in the negative group. The median age of the positive group was 56 years (age range 38\u0026ndash;79 years), and the median age of the negative group was 55 years (age range 24\u0026ndash;84 years). The median number of lymph nodes removed in the positive group was 10 (5\u0026ndash;23) in the left pelvic cavity, 10 (5\u0026ndash;28) in the right pelvic cavity, and 5 (1\u0026ndash;27) in the para-aortic region. In the negative group, the median number of lymph nodes removed was 9 (1\u0026ndash;34) in the left pelvic cavity, 8 (1\u0026ndash;27) in the right pelvic cavity, and 5 (1\u0026ndash;20) in the para-aortic region. There was no significant difference between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong the 33 patients with LNM, the frequency and number of LNM: left pelvis lymph nodes region appeared 20 times (1\u0026ndash;4), the right pelvis lymph nodes region appeared 19 times (1\u0026ndash;9), and 13 times (1\u0026ndash;7) were found in the para-aortic region. In addition, there were 3 patients with isolated para-aortic lymph node metastasis (no pelvic lymph node metastasis).\u003c/p\u003e \u003cp\u003eThe clinical and pathological variables of the 424 EEC patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the peripheral blood data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pathological characteristics of 424 EEC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal ligation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLive birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine leiomyoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyometrial invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical stromal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous layer/Adnexa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBMI, body mass index; LVSI, lymphovascular space invasion; MELF, microcystic, elongated, fragmented; LNM, Lymph node metastasis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePeripheral blood data of 424 EEC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM (P25-P75)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18(0.14\u0026ndash;0.23)\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 \u003cp\u003e42.86-581.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.50(117.16-193.05)\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 \u003cp\u003e0.59\u0026ndash;21.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07(1.57\u0026ndash;2.79)\u003c/p\u003e \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 \u003cp\u003e3.57\u0026ndash;535.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.64(11.60\u0026ndash;25.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60-547.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.30(8.21\u0026ndash;24.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u0026ndash;36.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70(1.19\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026ndash;9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.02(2.10\u0026ndash;3.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.94\u0026ndash;1242.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.75(49.21\u0026ndash;79.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; MLR, monocyte to lymphocyte ratio.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Did not conform to the normal distribution and were expressed as M (P25-P75).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate analysis of clinicopathological parameters\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis showed that histological grade, tumor size, depth of myometrial invasion, cervical stromal invasion, LVSI and MELF invasion pattern were associated with LNM in EEC patients(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0. 05). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 424 EEC patients according to LNM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLNM, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eχ2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eΡ\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epositive (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enegative (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;391)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(10.3)\u003c/p\u003e \u003cp\u003e21(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105(89.7)\u003c/p\u003e \u003cp\u003e286(93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(5.6)\u003c/p\u003e \u003cp\u003e19(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234(94.4)\u003c/p\u003e \u003cp\u003e157(89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(8.0)\u003c/p\u003e \u003cp\u003e7(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300(92.0)\u003c/p\u003e \u003cp\u003e91(92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal ligation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(9.8)\u003c/p\u003e \u003cp\u003e29(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(90.2)\u003c/p\u003e \u003cp\u003e354(92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLive birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(7.5)\u003c/p\u003e \u003cp\u003e2 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382(92.5)\u003c/p\u003e \u003cp\u003e9(81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(5.4)\u003c/p\u003e \u003cp\u003e24(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157(94.6)\u003c/p\u003e \u003cp\u003e234(90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(9.0)\u003c/p\u003e \u003cp\u003e25(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81(91.0)\u003c/p\u003e \u003cp\u003e310(92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine leiomyoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(8.1)\u003c/p\u003e \u003cp\u003e17(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182(91.9)\u003c/p\u003e \u003cp\u003e209(92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(11.6)\u003c/p\u003e \u003cp\u003e12(4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160(88.4)\u003c/p\u003e \u003cp\u003e231(95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(54.3)\u003c/p\u003e \u003cp\u003e14(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(45.7)\u003c/p\u003e \u003cp\u003e375(96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(81.2)\u003c/p\u003e \u003cp\u003e20(4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(18.8)\u003c/p\u003e \u003cp\u003e388(95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2cm\u003c/p\u003e \u003cp\u003e\u0026lt;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(12.1)\u003c/p\u003e \u003cp\u003e12(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152(87.9)\u003c/p\u003e \u003cp\u003e239(95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyometrial invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003cp\u003e\u0026lt;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(24.1)\u003c/p\u003e \u003cp\u003e12(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(75.9)\u003c/p\u003e \u003cp\u003e325(96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical stromal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(19.4)\u003c/p\u003e \u003cp\u003e27(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(80.6)\u003c/p\u003e \u003cp\u003e366(93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous layer/Adnexa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(18.8)\u003c/p\u003e \u003cp\u003e30(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(81.2)\u003c/p\u003e \u003cp\u003e378(92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBMI, body mass index; LVSI, lymphovascular space invasion; MELF, microcystic, elongated, fragmented; LNM, Lymph node metastasis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eUnivariate analysis of some indicators of peripheral blood\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis of preoperative peripheral blood indicators showed : CA125, CA199 and HE4 were associated with LNM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0. 05), while NLR, PLR, MLR, CEA and AFP were not associated with LNM. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of preoperative peripheral blood indexes in EEC patients according to LNM [Median(P25-P75) ]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eΡ\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82(1.56\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.08(1.57\u0026ndash;2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.406\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 \u003cp\u003e151.82(100.95\u0026ndash;196.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.47(117.36-190.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.702\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 \u003cp\u003e0.17(0.13\u0026ndash;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18(0.14\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \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 \u003cp\u003e37.59(17.62\u0026ndash;66.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.23(11.32\u0026ndash;23.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.38(8.71\u0026ndash;93.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.20(8.20-22.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94(1.31\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70(1.18\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03(1.83\u0026ndash;3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00(2.11\u0026ndash;3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.60(57.91\u0026ndash;91.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.20(48.45\u0026ndash;76.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; MLR, monocyte to lymphocyte ratio.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate logistic regression analysis of risk factors for LNM in EEC patients\u003c/h2\u003e \u003cp\u003eLogistic regression analysis showed that LVSI, MELF invasion pattern, myometrial invasion depth and CA125 were independent risk factors for LNM (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0. 05). (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of risk factors for LNM\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=\"left\" 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\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWald\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e 95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyometrial invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.969\u003c/p\u003e \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 \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eLVSI, lymphovascular space invasion; MELF, microcystic, elongated, fragmented.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe efficacy of diagnosing LNM by combining CA125 and depth of myometrial invasion or either alone\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe area under the ROC curve of CA125 and depth of myometrial invasion were 0.796 and 0.734 respectively, which were statistically significant(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The optimal diagnostic cut-off point of CA125 was 31.36 (U/mL), corresponding to the largest Youden index of 53.9%, and its positive likelihood ratio was 5.2. Among the four independent risk factors, the ROC curve was used to analyze the predictive efficacy of CA125 and myometrial invasion depth alone or in combination for LNM. According to the area under the ROC curve, the accuracy of diagnosing LNM by combining CA125 and depth of myometrial invasion is higher than that of either alone. (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea under the ROC curve\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyometrial invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredicted probability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA total of 424 patients with EEC were included in our study, with a LNM rate of 7.8%. Among them, 305 cases (71.9%) were at FIGO 2009 stage IA and 51 cases (12.0%) at stage IB, accounting for 83.9% (356/424) of stage I patients. CA125, myometrial invasion depth, LVSI and MELF pattern were independent risk factors for LNM in EEC patients. The optimal diagnostic cut-off point of preoperative CA125 was 31.36 (U/mL). Among the four independent risk factors, only myometrial invasion depth and CA125 could be evaluated preoperatively by imaging examination (magnetic resonance imaging, MRI) and serological examination. Therefore, by comparing the area under the ROC curve, we found that the combination of CA125 and depth of myometrial invasion was more accurate in diagnosing LNM than either alone. Our result further support a study in Korea [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], all confirm that MRI and CA125 are very important in the preoperative evaluation of endometrial cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In conclusion, low risk EEC also has the possibility of LNM, if the preoperative examination shows deep myometrial invasion and high CA125, it is necessary to pay attention to the possibility of LNM, and systematic lymph node resection is recommended. So we should pay attention to the evaluation of CA125 and myometrial invasion before operation, and we should pay attention to the identification of risk factors and do a more detailed hierarchical management for EEC patients.\u003c/p\u003e \u003cp\u003eLVSI positivity has long been considered to predict a poor prognosis, which was also confirmed in our study. Studies have shown that LVSI is a predictor of LNM in endometrial cancer and an important factor affecting the prognosis of patients with endometrial cancer [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A large Swedish study showed [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] that LVSI was the strongest independent risk factor for lymph node metastasis and survival in patients with endometrioid adenocarcinoma. In a study of 25,907 patients, Jorge et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] confirmed that LVSI was independently associated with lymph node metastasis and explored its risk degree(LVSI was associated with increased risks of LN metastasis by a magnitude of 3 to over 10-fold). However, at present, we cannot know whether LVSI is positive before surgery, and it would be of interest to investigate whether a reliable risk of lymph node involvement can be determined based on biopsy results. For some patients without endometrial cancer staging surgery, if LVSI is found to be positive, it may indicate the need for lymph nodes resection or adjuvant therapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMELF invasion was found in 16 cases, and 13 cases (13/16,81.2%) had LNM. Para-aortic lymph node metastasis occurred in 6 patients. It is suggested that MELF infiltration pattern is prone to LNM, even para-aortic lymph node metastasis. Studies have shown that MELF invasion is a unique invasion pattern of endometrial carcinoma, which is limited to low-grade invasive carcinoma of endometrioid type [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. MELF invasion is a risk factor for LNM in endometrial cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Lymph nodes from grade I EEC high-grade cellular budding or LVI should be examined for occult metastases, especially in the form of histiocyta-like cells[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By analyzing the location of positive lymph nodes in patients with LNM, it was found that 3 patients had no pelvic lymph node metastasis, only para-aortic lymph node metastasis. The clinical data of the three patients showed diffusely positive LVSI or MELF pattern invasion. It can be concluded that LVSI positive and MELF are both independent risk factors for lymph node metastasis, and are also more likely to have isolated para-aortic lymph node metastasis, which is worthy of further study with a larger sample size.\u003c/p\u003e \u003cp\u003eFurthermore, some studies have found that grade 2 lesions should be regarded as an important prognostic factor for the recurrence of low-risk endometrial cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Also, it has been pointed out that tumor diameter is related to LNM and recurrence in endometrial cancer patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and tumor size is an independent predictor of LNM and survival in early-stage endometrioid endometrial cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, our research results show that histological grade and tumor size are related to lymph node metastasis in EEC, but they are not independent risk factors. Some studies have indicated that pretreatment NLR and HE4 are predictors of lymph node metastasis in endometrial cancer patients [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our study aimed to verify this result in EEC, but unfortunately, it was not verified. The statistical results of NLR, PLR, MLR, and HE4 were not independent risk factors for EEC.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eOver-treatment is one of the problems existing in the treatment methods for endometrial cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, for EEC, we attempted to identify independent risk factors influencing LNM, in an effort to make some contributions to more detailed risk stratification. This study reminds us that preoperative MRI and other examinations are very important for the judgment of myometrial invasion depth, and the application of sentinel lymph nodes needs to be strengthened[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although histological grade G2, tumor size\u0026thinsp;\u0026ge;\u0026thinsp;2 cm, cervical stromal invasion, CA199 and HE4 are not independent risk factors in EEC, they are also worthy of our continuous attention. It may also be necessary to continue to increase sample research in the future. Our study is a single-center and retrospective study, maybe another multicenter, large-sample, and prospective study is required to provide a more valuable reference for clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLVSI, MELF invasion pattern, depth of myometrial invasion and CA125 are independent risk factors for LNM in EEC according to FIGO 2009. When these risk factors are present, LNM is more likely to occur. The accuracy of diagnosing LNM by combining CA125 and depth of myometrial invasion is higher than that of either alone. This study has certain reference value for predicting the risk of LNM and stratified management of treatment in patients with EEC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved and informed consent was waived by the institutional review board of \u0026nbsp;the Affiliated Hospital of Xuzhou Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLina Cao is the first author and corresponding author, who designed the study and wrote the manuscript. Xiaoyuan Lu supervised the manuscript and our study. Yijun Wang and Luyao Wang contributed to data collection. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOaknin A, Bosse TJ, Creutzberg CL, et al. Endometrial cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol. 2022;33(9):860-877. doi:10.1016/j.annonc.2022.05.009\u003c/li\u003e\n\u003cli\u003eSoslow RA, Tornos C, Park KJ, et al. 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Preoperative identification of a low-risk group for lymph node metastasis in endometrial cancer: a Korean gynecologic oncology group study. J Clin Oncol. 2012;30(12):1329-1334. doi:10.1200/JCO.2011.38.2416\u003c/li\u003e\n\u003cli\u003eAntonsen SL, Jensen LN, Loft A, et al. MRI, PET/CT and ultrasound in the preoperative staging of endometrial cancer - a multicenter prospective comparative study. Gynecol Oncol. 2013;128(2):300-308. doi:10.1016/j.ygyno.2012.11.025\u003c/li\u003e\n\u003cli\u003eWakayama A, Kudaka W, Matsumoto H, et al. Lymphatic vessel involvement is predictive for lymph node metastasis and an important prognostic factor in endometrial cancer. Int J Clin Oncol. 2018;23(3):532-538. doi:10.1007/s10147-017-1227-6\u003c/li\u003e\n\u003cli\u003eAyhan A, Şahin H, Sari ME, Yal\u0026ccedil;in I, Haberal A, Meydanli MM. Prognostic significance of lymphovascular space invasion in low-risk endometrial cancer. 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Best Pract Res Clin Obstet Gynaecol. 2024;94:102483. doi:10.1016/j.bpobgyn.2024.102483\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Low-grade endometrioid carcinoma(EEC), Lymph node metastasis(LNM), Risk factors, Predicting","lastPublishedDoi":"10.21203/rs.3.rs-5763156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5763156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eTo explore the lymph node metastasis (LNM) of low-grade endometrioid carcinoma (EEC) and its related risk factors, and to analyze the efficacy of related risk factors in predicting LNM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data of 424 EEC patients who underwent endometrial cancer staging surgery from January 2019 to June 2024 were retrospectively analyzed. Univariate and multivariate logistic regression were used to analyze the related factors of LNM. The receiver operating characteristic (ROC) curve was drawn to analyze the efficacy of related independent risk factors in predicting LNM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe rate of LNM was 7.8% (33/424). Univariate analysis showed that histological grade, tumor size, depth of myometrial, cervical stromal, lymphovascular space invasion (LVSI), microcystic, elongated, fragmented (MELF), carbohydrate antigen 125 (CA125), carbohydrate antigen 199 (CA199)and human epididymis protein 4 (HE4) were associated with LNM of EEC. Logistic regression analysis showed that LVSI, MELF, depth of myometria and CA125were independent risk factors for LNM. The ROC curve area of CA125 and depth of myometria was 0.796 and 0.734. The best cut-off point of CA125 was 31.36 (U/mL), corresponding to the largest Youden index of 53.9%, and its positive likelihood ratio was 5.2. The accuracy of diagnosing LNM by combining CA125 and depth of myometrial is higher than that of either alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e LNM is more likely to occur when there are risk factors such as LVSI positive, MELF invasion pattern, myometrial invasion depth ≥50% and CA125. The accuracy of CA125 combined with depth of myometrial in the diagnosis of LNM is higher than that of either alone. This study has a certain reference value for predicting the risk of LNM and stratified management and treatment for EEC patients.\u003c/p\u003e","manuscriptTitle":"Analysis of Lymph Node Metastasis and Risk Factors in 424 Patients with low-grade endometrioid carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 12:45:29","doi":"10.21203/rs.3.rs-5763156/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7f047c8-805e-4aee-95e7-984d799cb5df","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T12:45:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 12:45:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5763156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5763156","identity":"rs-5763156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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