Novel prognostic score for endometrial cancer

Novel prognostic score for endometrial cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Novel prognostic score for endometrial cancer Tomoka Maehana, Naoki Kawahara, Junya Kamibayashi, Motoki Matsuoka, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4709115/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recently, there have been an increasing number of reports on the association between inflammatory markers and the prognosis of malignant tumors. However, the current indicators have limited accuracy. We aimed to develop a new scoring system for predicting endometrial cancer recurrence using inflammatory markers, tumor markers, and histological diagnosis. Patients with primary, previously untreated, and suspected endometrial cancer who underwent surgery at the Nara Medical University Hospital between January 2007 and December 2020 were included and followed up until March 2024. Items were divided into positive and negative using scores based on cutoff values and placed into the new scoring system, the endometrial tumor-related (ETR) score. We found that positive postoperative histological examination of lymph node metastasis and myometrial invasion, high levels of carcinoembryonic antigen and D-dimer in preoperative blood tests, and a large difference in preoperative and postoperative white blood cell counts were significantly associated with recurrence. The prediction of recurrence using the ETR score was superior to that using the International Federation of Gynecology and Obstetrics staging system, which is considered the best prognostic factor for survival. The ETR score is a significant prognostic marker of recurrence in patients who have undergone lymphadenectomy, with complete surgical tumor removal. Endometrial cancer Recurrence Disease-free survival rate ETR score D-dimer White blood cell count Figures Figure 1 Figure 2 Figure 3 Introduction Endometrial cancer (EC) is the second most prevalent gynecological cancer after cervical cancer in women [ 1 , 2 ]. The morbidity of EC has been increasing globally; in 2018, almost 90,000 deaths due to EC were reported worldwide [ 3 ]. EC is known to recur in 18% of all patients [ 4 ], and the International Federation of Gynecology and Obstetrics (FIGO) staging system is considered one of the best prognostic factors for survival [ 5 ]. The 5-year disease-free survival rates have been 85% for stage I, 75% for stage II, 45% for stage III, and 25% for stage IV [ 5 ]. In addition, the histological type of EC is known to correlate with the prognosis [ 6 ]. These include endometrioid carcinoma, serous carcinoma, clear cell carcinoma, mixed carcinoma, undifferentiated carcinoma, carcinosarcoma, other unusual types, and gastrointestinal mucinous carcinomas [ 6 ]. These histological types are divided into two groups, non-aggressive and aggressive, based on their prognostic value [ 6 ]. Non-aggressive types include endometrioid carcinoma grades 1 and 2, whereas other types are aggressive [ 6 , 7 ]. EC is typically treated with surgery, including hysterectomy and bilateral salpingo-oophorectomy [ 8 , 9 ]. Lymphadenectomy is also known to be associated with prognosis in patients with intermediate- and high-risk EC [ 8 , 10 , 11 ]. In recent years, there have been an increasing number of reports on the association between inflammatory markers and the prognosis of malignant tumors [ 12 , 13 ]. The inflammatory markers used include the systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), all of which are calculated using blood cell counts [ 12 – 14 ]. The Glasgow Prognostic Score (GPS) is an inflammation-based prognostic marker calculated using C-reactive protein and albumin levels [ 15 ]. We previously reported the relationship between the SII, NLR, and PLR and the prognosis of EC in 2021 and concluded that elevated SII is a better indicator of overall survival and progression-free survival in patients with EC than PLR or NLR [ 12 ]. Current indicators have limited accuracy because they rely only on one point in time before surgery. To enhance accuracy, we aimed to develop a prediction system using preoperative and postoperative data rather than just one pretreatment point. To ensure consistency in clinical background, we focused on cases that included lymphadenectomy. We aimed to develop a new scoring system to predict EC recurrence after complete tumor removal, including lymphadenectomy. Results Patients A total of 521 patients were suspected of having EC during the study period, of whom 230 met the inclusion criteria (Fig. 1 ). The median patient age was 59 years (range, 23–79 years), and the median follow-up period was 84.5 months (range, 12–195 months). The patients’ peripheral blood data were collected before the operation, and the median number of days until the operation was 37 (range, 1–92 days). In addition, the postoperative blood data before the adjuvant therapy were collected, and the median number of days between the operation date and the blood test was 26 days (range, 14–59 days). The overall recurrence rate was 15.7% (n = 36). The demographic and clinical characteristics of the study cohort are shown in Table 1 . The surgeries performed in the study cohort are outlined in Supplementary Table S1 . Table 1 Clinical characteristics of the study cohort. Total n = 230 No recurrence n = 194 Recurrence n = 36 p - value Age (years) a 59 (23–79) 59 (23–79) 61.5 (43–77) 0.080 BMI (kg/m 2 ) a 23.65 (15.1–43.5) 24.1 (15.1–43.5) 22.8 (17.6–32.8) 0.022* Parity 0 ≥1 50 180 42 152 8 28 0.939 FIGO Stage I II III 169 23 38 152 19 23 17 4 15 < 0.001 *** Tumor subtype EM G1/G2 EM G3 CCC Serous carcinoma Carcinosarcoma Undifferentiated Mixed type Others 155 25 8 7 10 6 17 2 135 20 5 4 7 6 15 2 20 5 3 3 3 0 2 0 0.340 Myometrial invasion < 1/2 ≥ 1/2 140 90 124 70 16 20 0.028 * Lymph vascular invasion Positive Negative 83 147 66 128 17 19 0.131 Ascites cytology Positive Negative 42 183 33 156 9 27 0.289 Lymph node metastasis Positive Negative 201 29 178 16 13 23 0.002 ** Adjuvant chemotherapy Yes No 146 84 119 75 27 9 0.098 BMI: body mass index, FIGO: The international federation of gynecology and obstetrics, EM: Endometrioid carcinoma, G1: Grade 1, G2: Grade 2, G3: Grade 3, CCC: clear cell carcinoma, a : median (range). * : p < 0.05, ** : p < 0.01, *** : p < 0.001 Candidates predicting the recurrence of EC The results of the ROC curve based on recurrence were used to determine the value of the new scoring system in predicting EC recurrence. Candidates varied according to patient characteristics; histological findings after the surgery; and preoperative and postoperative serum data, including tumor markers, blood cell counts, differential counts of leukocytes, albumin, and D-dimer levels. The ROC analysis showed that lymph node metastasis, myometrial invasion, preoperative carcinoembryonic antigen (CEA) (ng/mL), preoperative D-dimer (µg/mL), and subtracted value of postoperative from preoperative white blood cell (WBC) counts (/µL) significantly predicted EC recurrence (Table 2 ). Table 2 Cutoff values for predicting disease-free survival. Cut-off value p -value AUC 95% CI Sensitivity Specificity Lymph node metastasis - 0.013* 0.639 0.530–0.749 0.361 0.918 Myometrial invasion - 0.014* 0.620 0.525–0.715 0.556 0.639 preoperative CEA (ng/mL) 2.4 0.010* 0.640 0.534–0.747 0.611 0.706 preoperative D-dimer (µg/mL) 1.1 0.003** 0.657 0.533–0.761 0.500 0.820 WBC (pre – post operation) (/µL) 1050 0.010* 0.637 0.533–0.741 0.750 0.572 AUC: area under the curve, CI: confidential interval, CEA: carcinoembryonic antigen, WBC: white blood cell. * : p < 0.05, ** : p < 0.01 Evaluation to predict the recurrence of EC using past prognostic indicators The results of the ROC curve analysis based on recurrence were used to evaluate past prognostic indicators for predicting EC recurrence. The cutoff value was calculated using the highest Youden index. None of the past prognostic indicators showed significant differences between the recurrent and nonrecurrent groups (Table 3 ). However, the new scoring system, the endometrial tumor-related (ETR) score, showed significant differences between the recurrent and nonrecurrent groups (Table 3 ). Some of the study cohort groups had missing data for calculating the past prognostic indicators. The percentages of analyzed and missing data are shown (Supplementary Table S2). Table 3 Cutoff values of past predictive indicators to predict disease-free survival. Cut-off value p -value AUC 95% CI Sensitivity Specificity SII 574 0.102 0.593 0.482–0.704 0.706 0.508 NLR 2.72 0.070 0.599 0.492–0.706 0.618 0.608 MLR 0.41 0.500 0.537 0.429–0.645 0.147 0.931 PLR 121.9 0.084 0.587 0.488–0.686 0.941 0.286 GPS 1.5 0.215 0.569 0.460–0.678 0.194 0.948 ETR score 2.5 < 0.001 0.766 0.672–0.861 0.639 0.840 AUC: area under the curve, CI: confidential interval, PPV: positive predictive value, NPV: negative predictive value, SII: systemic immune-inflammation index, NLR: neutrophil to lymphocyte ratio, MLR: monocyte to lymphocyte ratio, PLR: platelet to lymphocyte ratio, GPS: Glasgow prognostic score, ETR score: endometrial cancer-tumor related score. Evaluation of the new scoring system The ROC curve showed that the new scoring system was a better prognostic tool than the FIGO staging system for predicting EC recurrence (Fig. 2 ). The cutoff value of the ETR score was calculated as 2.5, using the Youden index. According to this cutoff value, we considered > 3 points as the poor group, with a sensitivity of 63.9% and a specificity of 84.0% for diagnosing recurrence. In addition, we used this cutoff point to evaluate the sensitivity and specificity for predicting death. Sensitivity and specificity were 70.0% and 81.0%, respectively. Kaplan–Meier life table analysis revealed significant differences in disease-free survival and overall survival using a cutoff value of 3 (Fig. 3 a and 3 b). Cox regression analyses demonstrated that the ETR score was a significant independent prognostic factor affecting the disease-free survival of patients with EC (Table 4 ). Table 4 Univariate and multivariate analyses predicting recurrence. patients Univariate analysis Cox multivariate analysis n HR p -value HR 95% CI p -value FIGO stage ≥ Ⅲ < Ⅰ – Ⅱ 38 192 5.311 < 0.001*** Lymphovascular invasion Yes No 83 147 1.735 0.133 Ascites cytology Positive Negative 42 183 1.576 0.290 Myometrial invasion ≥ 1/2 < 1/2 90 140 2.214 0.030* Lymph node metastasis Yes No 29 201 6.288 < 0.001*** Preoperative CEA ≥ 2.4 < 2.4 79 151 3.777 < 0.001*** Preoperative d-dimer ≥ 1.1 < 1.1 53 177 4.543 < 0.001*** Preoperative-postoperative WBC (/µL) ≥ 1,050 < 1,050 110 120 4.012 < 0.001*** ETR score ≥ 3 < 3 176 54 9.303 < 0.001*** 9.017 4.127–19.701 < 0.001 HR: hazard ratio, CI: confidential interval, FIGO: The international federation of gynecology and obstetrics, CEA: carcinoembryonic antigen, WBC: white blood cell, ETR: endometrial cancer-tumor related. Discussion In this study, we successfully developed a new scoring system, the ETR score, to predict EC recurrence after radical surgery. The score comprised lymph node metastasis, myometrial invasion, preoperative CEA levels, preoperative D-dimer levels, and WBC difference (pre–post). The strength of the ETR score is that it is easy to calculate because blood tests and histological findings have already been used worldwide. Serum CEA, an item of the ETR score, is a diagnostic and prognostic marker of broad-spectrum malignant tumors, especially colon and rectal cancers [ 16 , 17 ]. Postoperative CEA levels reflect recurrence or distant metastasis of EC [ 18 ], suggesting a relationship between EC prognosis and CEA level. In this study, preoperative CEA levels were included in the predictive scoring system for recurrence, suggesting that these values predict recurrence. There have been reports linking high WBC counts to aggressive tumors or poor prognosis. According to a large cohort study conducted in the UK Biobank, elevated WBC counts may indicate an overly active inflammatory response, which could contribute to the eventual onset of certain types of cancer [ 19 ]. In endometrial neoplasia, WBC counts were significantly higher in patients with cancer than in those with hyperplasia, according to a study that compared the hyperplasia, EC, and control groups [ 20 ]. Based on a few reports suggesting the usefulness of pretreatment peripheral WBC counts, this difference may help predict the prognosis. Our previous study showed that this difference contributes to the prognosis of ovarian cancer by comparing presurgical and postsurgical analyses [ 21 , 22 ]. This study reaffirmed the usefulness of the differences in WBC counts in predicting recurrence. Serum D-dimer level, an item of the ETR score, is a well-known biomarker of thrombosis, such as pulmonary embolism and venous thrombosis [ 23 ]. However, it is also known as a prognostic marker for several malignancies, such as ovarian cancer [ 24 ], breast cancer [ 25 ], lung cancer [ 26 ], and other cancers [ 27 – 31 ]. According to a previous study, it is reasonable to include serum D-dimer levels in the new scoring system. This study had a few limitations. First, a bias might exist owing to the nature of a retrospective and single-center study. Second, although serous and clear cell carcinomas have poor prognoses, the study cohort did not show a significant difference in histological types. Such patients ordinarily harbor an advanced stage and then receive chemotherapy rather than surgical treatment. ETR score could not reflect these small pathological groups. In conclusion, the ETR score, which is composed of the presence of lymph node metastasis and myometrial invasion, preoperative CEA and D-dimer levels, and the difference in (pre–post) WBC levels, is a good prognostic marker for patients with EC who have undergone complete surgery. Prospective multicenter studies are warranted to validate our findings. Methods Patients A list of patients with primary, previously untreated, and suspected EC who underwent surgery at the Nara Medical University Hospital between January 2007 and December 2020 was generated from our institutional registry. The patients were followed up until March 2024. Informed consent for the use of the patients’ clinical data for research was obtained from all subjects at the first hospitalization. After approval by the Ethics Review Committee of the Nara Medical Hospital, the opt-out form was provided through our institutional homepage. This study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Institutional Ethics Committee of Nara Medical University Hospital (protocol code: 3603). Patients with suspected EC were included in this study. The inclusion criteria were as follows: (1) histologically confirmed EC after surgery, (2) lymphadenectomy, and (3) did not undergo chemotherapy or radiotherapy before the first surgery. The exclusion criteria were as follows: (1) stage IV, (2) combined with other malignant tumors or hematologic diseases, (3) lost to follow-up, and (4) insufficient preoperative and postoperative serum data, which included no blood tests within 14–60 days or performed while infection occurred. Written consent for the use of the patient’s clinical data for research was obtained at the first hospitalization, and after approval by the Ethics Review Committee of the Nara Medical Hospital, the opt-out form was provided through our institutional homepage. Collection of candidates predicting recurrence The following data were collected through a chart review of the patients’ medical records: age; body mass index; parity; and postoperative diagnosis, including the FIGO stage, histological type, myometrial invasion, lymphovascular invasion, ascites cytology, lymph node metastasis, and distant metastasis. In addition, data concerning the use of adjuvant chemotherapy and the results of preoperative and postoperative blood tests were collected. Examination of prognosis using past prognostic indicators We examined the efficacy of past inflammation-based prognostic indices such as SII, NLR, MLR, PLR, and GPS. These indicators were calculated using the following formulae: SII = platelet count×neutrophil count/lymphocyte count, NLR = neutrophil count/lymphocyte count, MLR = monocyte count/lymphocyte count, and PLR = platelet count/lymphocyte count. If an elevated C-reactive protein level (> 1.0 mg/dL) and hypoalbuminemia (< 3.5 g/dL) were present, the GPS was 2. Patients with only one of these were assigned a score of 1, and those with none were assigned a score of 0. Statistical analysis All statistical analyses were performed using SPSS version 29.0 (IBM Corp., Armonk, NY, USA). Differences in each factor were compared using Student’s t-test or the Mann–Whitney U test after assessing whether the distribution was normal. The receiver operating characteristic (ROC) curve analysis was performed to determine the cutoff value for predicting poor prognosis. The cutoff value was based on the highest Youden index (i.e., sensitivity + specificity–1). Next, logistic regression analyses were used to assess the risk factors for recurrence and death. Kaplan–Meier life table analysis and log-rank tests were used to assess the disease-free survival and overall survival rates. A two-sided p < 0.05 was considered statistically significant. Multivariate analysis of prognostic factors for PFS and OS was performed using the Cox proportional hazard regression model. Declarations Data availability The data presented in this study are available on request from the corresponding author. Acknowledgements: None Author contributions: Conceptualization, T.M.; methodology, T.M. and N.K.; validation, T.M. and N.K.; formal analysis, T.M. and N.K.; investigation, T.M. and N.K.; resources, T.M., N.K., R.K., and F.K.; data curation, T.M., N.K., and S.S.; writing—original draft preparation, T.M. and N.K.; writing—review and editing, T.M., N.K., R.K., and F.K.; visualization, T.M., J.K., M.M., and K.W.; supervision, F.K.; project administration, F.K. All authors have read and agreed to the published version of the manuscript. Competing interests: The authors declare no competing interests. References Cao, M., Li, H., Sun, D. & Chen, W. Cancer burden of major cancers in China: A need for sustainable actions. Cancer Commun. (Lond.) 40 , 205–210 (2020). Ferlay, J. et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. Eur. J. Cancer 103 , 356–387 (2018). Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 coun­tries. CA Cancer J. Clin. 68 , 394–424 (2018). Capozzi, V. A. et al. Pattern of recurrence in endometrial cancer. The murderer always returns to the scene of the crime. Eur. J. Surg. Oncol. 50 , 107985 (2024). Sabater, S. et al. Does postoperative irradiation improve survival in early-stage endometrial cancer? Brachytherapy 17 , 912–921 (2018). Berek, J. S. et al. FIGO staging of endometrial cancer: 2023. J. Gynecol. Oncol. 34 , e85 (2023). Amant, F. et al. Endometrial cancer. Lancet 366 , 491–505 (2005). Brooks, R. A. et al. Current recommendations and recent progress in endometrial cancer. CA Cancer J. Clin. 69 , 258–279 (2019). Crosbie, E. J. et al. Endometrial cancer. Lancet 399 , 1412–1428 (2022). Chan, J. K. et al. Therapeutic role of lymph node resection in endometrioid corpus cancer: a study of 12,333 patients. Cancer 107 , 1823–1830 (2006). Todo, Y. et al. Survival effect of para-aortic lymphadenectomy in endometrial cancer (SEPAL study): a retrospective cohort analysis. Lancet 375 , 1165–1172 (2010). Matsubara, S., Mabuchi, S., Takeda, Y., Kawahara, N. & Kobayashi, H. Prognostic value of pre-treatment systemic immune-inflammation index in patients with endometrial cancer. PLoS One 16 , e0248871 (2021). Nøst, T. H. et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur. J. Epidemiol. 36 , 841–848 (2021). Leng, J., Wu, F. & Zhang, L. Prognostic Significance of Pretreatment Neutrophil-to- Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, or Monocyte-to-Lymphocyte Ratio in Endometrial Neoplasms: A Systematic Review and Meta-analysis. Front. Oncol. 12 , 734948 (2022). Zhu, J., Wang, H., Liu, C. C., Lu, Y. & Tang, H. The Glasgow Prognostic Score (GPS) is a novel prognostic indicator in advanced epithelial ovarian cancer: a multicenter retrospective study. J. Cancer Res. Clin. Oncol. 142 , 2339–2345 (2016). Kawahara, N., Miyake, R., Yamanaka, S. & Kobayashi, H. A Novel Predictive Tool for Discriminating Endometriosis Associated Ovarian Cancer from Ovarian Endometrioma: The R2 Predictive Index. Cancers (Basel) 13 , 3829 (2021). Mizuno, H. et al. Optimal cutoff value of preoperative CEA and CA19-9 for prognostic significance in patients with stage II/III colon cancer. Langenbecks Arch. Surg. 406 , 1987–1997 (2021). Kozakiewicz, B., Chądzyńska, M., Dmoch-Gajzlerska, E. & Stefaniak, M. Monitoring the treatment outcome in endometrial cancer patients by CEA and TATI. Tumour Biol. 37 , 9367–9374 (2016). Song, M., Graubard, B. I., Loftfield, E., Rabkin, C. S. & Engels, E. A. White Blood Cell Count, Neutrophil-to-Lymphocyte Ratio, and Incident Cancer in the UK Biobank. Cancer Epidemiol. Biomarkers Prev. 33 , 821–829 (2024). Acmaz, G. et al. Are neutrophil/lymphocyte and platelet/lymphocyte ratios associated with endometrial precancerous and cancerous lesions in patients with abnormal uterine bleeding? Asian Pac. J. Cancer Prev. 15 , 1689–1692 (2014). Kawahara, N. et al. The prognosis predictive score around primary debulking surgery (PPSP) improves diagnostic efficacy in predicting the prognosis of ovarian cancer. Sci. Rep. 12 , 22636 (2022). Kawahara, N. et al. The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients. Cancers (Basel) 15 , 5062 (2023). van Es, N. et al. Diagnostic management of acute pulmonary embolism: a prediction model based on a patient data meta-analysis. Eur. Heart J. 44 , 3073–3081 (2023). Yamada, Y. et al. Preoperative plasma D-dimer level is a useful prognostic marker in ovarian cancer. J. Obstet. Gynaecol. 40 , 102–106 (2020). Batschauer, A. P. B. et al. D-dimer as a possible prognostic marker of operable hormone receptor-negative breast cancer. Ann. Oncol. 21 , 1267–1272 (2010). Chen, C. et al. Plasma D-dimer and interleukin-6 are associated with treatment response and progression-free survival in advanced NSCLC patients on anti-PD-1 therapy. Cancer Med. 12 , 15831–15840 (2023). Stender, M. T., Larsen, T. B., Sørensen, H. T. & Thorlacius-Ussing, O. Preoperative plasma D-dimer predicts 1-year survival in colorectal cancer patients with absence of venous thromboembolism (VTE): a prospective clinical cohort study. J. Thromb. Haemost. 10 , 2027–2031 (2012). Diao, D. et al. D-dimer: not just an indicator of venous thrombosis but a predictor of asymptomatic hematogenous metastasis in gastric cancer patients. PLoS One 9 , e101125 (2014). Diao, D. et al. D-dimer is an essential accompaniment of circulating tumor cells in gastric cancer. BMC Cancer 17 , 56 (2017). Wang, Y. & Wang, Z. Predictive value of plasma D-dimer levels in patients with advanced non-small-cell lung cancer. Onco Targets Ther. 8 , 805–808 (2015). Bi, X. W. et al. High pretreatment D-dimer levels correlate with adverse clinical features and predict poor survival in patients with natural killer/T-cell lymphoma. PLoS One 11 , e0152842 (2016). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4709115","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334421016,"identity":"6a368430-2432-4bd9-a01d-12db8d687108","order_by":0,"name":"Tomoka Maehana","email":"","orcid":"","institution":"Nara Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tomoka","middleName":"","lastName":"Maehana","suffix":""},{"id":334421017,"identity":"9b6964b0-42a7-408b-a379-4d7dbe61d5c4","order_by":1,"name":"Naoki Kawahara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIie3RMUsDMRTA8QeB3BKcW070K0QCVbl+mBwHyZJMLg6CneqSD3NO4hYI1EW5NWAHu2RyqA5yIBRTBUHwrtwmkv+W4Zfw8gBSqb8ZAt5uDr4PX5EdpDSWDSSAbTn7SXo6zpyjT2Qpb7KH1as/L4BatFrD/rKTnBpRcX4S9K2RLFf3MhLMRkBCJ6FWMcsJ0rUVkOu5u6wtTOIsrps0z5FgJGkT0LveuHhJ9tZPvGKcY8epFzjXsy0hO17xoaKlEUe1D7hQCwljR85GvG+WpnLjtp0e0kagR3VRwN7d1fX6xXT/2C9tVxOXO4R81g4nqVQq9W/7AG0vVmM7T4pgAAAAAElFTkSuQmCC","orcid":"","institution":"Nara Medical University","correspondingAuthor":true,"prefix":"","firstName":"Naoki","middleName":"","lastName":"Kawahara","suffix":""},{"id":334421018,"identity":"ab686cb3-e44e-4eab-94c2-e30ab78fffba","order_by":2,"name":"Junya Kamibayashi","email":"","orcid":"","institution":"Nara Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junya","middleName":"","lastName":"Kamibayashi","suffix":""},{"id":334421019,"identity":"c777a858-b3f3-4434-a7ee-b1c3b55c1f8f","order_by":3,"name":"Motoki Matsuoka","email":"","orcid":"","institution":"Nara Medical University","correspondingAuthor":false,"prefix":"","firstName":"Motoki","middleName":"","lastName":"Matsuoka","suffix":""},{"id":334421020,"identity":"0615b4d3-6ac2-48c9-9055-0eeed9cad13e","order_by":4,"name":"Keita Waki","email":"","orcid":"","institution":"Nara Medical University","correspondingAuthor":false,"prefix":"","firstName":"Keita","middleName":"","lastName":"Waki","suffix":""},{"id":334421021,"identity":"b72c724b-2d79-42e1-8e85-442cf6245361","order_by":5,"name":"Sumire Sugimoto","email":"","orcid":"","institution":"Nara Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sumire","middleName":"","lastName":"Sugimoto","suffix":""},{"id":334421024,"identity":"1dec4ced-fdb2-43d6-8119-090367e47758","order_by":6,"name":"Ryuji Kawaguchi","email":"","orcid":"","institution":"Nara Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ryuji","middleName":"","lastName":"Kawaguchi","suffix":""},{"id":334421025,"identity":"6ef42976-df82-4bf0-99a3-76402de56678","order_by":7,"name":"Fuminori Kimura","email":"","orcid":"","institution":"Nara Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fuminori","middleName":"","lastName":"Kimura","suffix":""}],"badges":[],"createdAt":"2024-07-09 04:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4709115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4709115/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62156784,"identity":"43946ecb-ba41-4008-9773-1bfd91371b40","added_by":"auto","created_at":"2024-08-09 21:11:46","extension":"tiff","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118829,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant selection. Of the 338 women who met the inclusion criteria, 108 were excluded as they met the exclusion criteria, while 230 patients participated in the study.\u003c/p\u003e","description":"","filename":"Figure1ver.21.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4709115/v1/6f0fcb20baab395f3c3a3823.tiff"},{"id":62157974,"identity":"af01459a-8346-4e10-9b14-67d2a3b60dc7","added_by":"auto","created_at":"2024-08-09 21:27:47","extension":"tiff","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117709,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the FIGO stage and new ETR score. The cutoff value was calculated as 2.5 using the Youden index. Using this cutoff value, the sensitivity and specificity were 63.9% and 84.0%, respectively. ROC, receiver operating characteristic curve; FIGO, International Federation of Gynecology and Obstetrics; ETR, endometrial tumor-related score.\u003c/p\u003e","description":"","filename":"Figure2ver.2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4709115/v1/91398ed22df538cc046727ea.tiff"},{"id":62156787,"identity":"01b6b292-b7a1-40e4-bb27-eaf63e5aea0c","added_by":"auto","created_at":"2024-08-09 21:11:47","extension":"tiff","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121940,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier life table analysis of disease-free survival in patients with endometrial cancer according to the ETR score using a cutoff value of 3 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01). FIGO, International Federation of Gynecology and Obstetrics; ETR, endometrial tumor-related score.\u003c/p\u003e","description":"","filename":"Figure3ver.2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4709115/v1/4400356f8720505af697b582.tiff"},{"id":63602516,"identity":"5055d4d0-87c9-49cc-8eaa-20b761741128","added_by":"auto","created_at":"2024-08-30 05:44:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1093213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4709115/v1/09e0441e-a2ff-485c-8bf5-0c6d5c3345f2.pdf"},{"id":62157522,"identity":"e8234c12-ccdd-4757-b4b8-88be60658276","added_by":"auto","created_at":"2024-08-09 21:19:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14594,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4709115/v1/739bc8e15ce9fbb6475b6b7b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel prognostic score for endometrial cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is the second most prevalent gynecological cancer after cervical cancer in women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The morbidity of EC has been increasing globally; in 2018, almost 90,000 deaths due to EC were reported worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. EC is known to recur in 18% of all patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and the International Federation of Gynecology and Obstetrics (FIGO) staging system is considered one of the best prognostic factors for survival [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The 5-year disease-free survival rates have been 85% for stage I, 75% for stage II, 45% for stage III, and 25% for stage IV [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, the histological type of EC is known to correlate with the prognosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These include endometrioid carcinoma, serous carcinoma, clear cell carcinoma, mixed carcinoma, undifferentiated carcinoma, carcinosarcoma, other unusual types, and gastrointestinal mucinous carcinomas [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These histological types are divided into two groups, non-aggressive and aggressive, based on their prognostic value [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Non-aggressive types include endometrioid carcinoma grades 1 and 2, whereas other types are aggressive [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. EC is typically treated with surgery, including hysterectomy and bilateral salpingo-oophorectomy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lymphadenectomy is also known to be associated with prognosis in patients with intermediate- and high-risk EC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, there have been an increasing number of reports on the association between inflammatory markers and the prognosis of malignant tumors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The inflammatory markers used include the systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), all of which are calculated using blood cell counts [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Glasgow Prognostic Score (GPS) is an inflammation-based prognostic marker calculated using C-reactive protein and albumin levels [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We previously reported the relationship between the SII, NLR, and PLR and the prognosis of EC in 2021 and concluded that elevated SII is a better indicator of overall survival and progression-free survival in patients with EC than PLR or NLR [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent indicators have limited accuracy because they rely only on one point in time before surgery. To enhance accuracy, we aimed to develop a prediction system using preoperative and postoperative data rather than just one pretreatment point. To ensure consistency in clinical background, we focused on cases that included lymphadenectomy. We aimed to develop a new scoring system to predict EC recurrence after complete tumor removal, including lymphadenectomy.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA total of 521 patients were suspected of having EC during the study period, of whom 230 met the inclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median patient age was 59 years (range, 23\u0026ndash;79 years), and the median follow-up period was 84.5 months (range, 12\u0026ndash;195 months). The patients\u0026rsquo; peripheral blood data were collected before the operation, and the median number of days until the operation was 37 (range, 1\u0026ndash;92 days). In addition, the postoperative blood data before the adjuvant therapy were collected, and the median number of days between the operation date and the blood test was 26 days (range, 14\u0026ndash;59 days). The overall recurrence rate was 15.7% (n\u0026thinsp;=\u0026thinsp;36). The demographic and clinical characteristics of the study cohort are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The surgeries performed in the study cohort are outlined in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\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 characteristics of the study cohort.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eTotal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;230\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo recurrence\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\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\u003eAge (years)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (23\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (23\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.5 (43\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\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)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.65 (15.1\u0026ndash;43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1 (15.1\u0026ndash;43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.8 (17.6\u0026ndash;32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e\u0026ge;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIGO Stage\u003c/p\u003e \u003cp\u003eI\u003c/p\u003e \u003cp\u003eII\u003c/p\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor subtype\u003c/p\u003e \u003cp\u003eEM G1/G2\u003c/p\u003e \u003cp\u003eEM G3\u003c/p\u003e \u003cp\u003eCCC\u003c/p\u003e \u003cp\u003eSerous carcinoma\u003c/p\u003e \u003cp\u003eCarcinosarcoma\u003c/p\u003e \u003cp\u003eUndifferentiated\u003c/p\u003e \u003cp\u003eMixed type\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyometrial invasion\u003c/p\u003e \u003cp\u003e\u0026lt; 1/2\u003c/p\u003e \u003cp\u003e\u0026ge; 1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph vascular invasion\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites cytology\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201\u003c/p\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI: body mass index, FIGO: The international federation of gynecology and obstetrics, EM: Endometrioid carcinoma, G1: Grade 1, G2: Grade 2, G3: Grade 3, CCC: clear cell carcinoma, \u003csup\u003ea\u003c/sup\u003e: median (range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e*\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCandidates predicting the recurrence of EC\u003c/h2\u003e \u003cp\u003eThe results of the ROC curve based on recurrence were used to determine the value of the new scoring system in predicting EC recurrence. Candidates varied according to patient characteristics; histological findings after the surgery; and preoperative and postoperative serum data, including tumor markers, blood cell counts, differential counts of leukocytes, albumin, and D-dimer levels. The ROC analysis showed that lymph node metastasis, myometrial invasion, preoperative carcinoembryonic antigen (CEA) (ng/mL), preoperative D-dimer (\u0026micro;g/mL), and subtracted value of postoperative from preoperative white blood cell (WBC) counts (/\u0026micro;L) significantly predicted EC recurrence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eCutoff values for predicting disease-free survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.530\u0026ndash;0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.918\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u0026ndash;0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative CEA (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.534\u0026ndash;0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative D-dimer\u003c/p\u003e \u003cp\u003e(\u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.533\u0026ndash;0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (pre \u0026ndash; post operation)\u003c/p\u003e \u003cp\u003e(/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.533\u0026ndash;0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAUC: area under the curve, CI: confidential interval, CEA: carcinoembryonic antigen, WBC: white blood cell. \u003csup\u003e*\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation to predict the recurrence of EC using past prognostic indicators\u003c/h2\u003e \u003cp\u003eThe results of the ROC curve analysis based on recurrence were used to evaluate past prognostic indicators for predicting EC recurrence. The cutoff value was calculated using the highest Youden index. None of the past prognostic indicators showed significant differences between the recurrent and nonrecurrent groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the new scoring system, the endometrial tumor-related (ETR) score, showed significant differences between the recurrent and nonrecurrent groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Some of the study cohort groups had missing data for calculating the past prognostic indicators. The percentages of analyzed and missing data are shown (Supplementary Table S2).\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\u003eCutoff values of past predictive indicators to predict disease-free survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u0026ndash;0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.508\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\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.492\u0026ndash;0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.608\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\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u0026ndash;0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.931\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\u003e121.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.488\u0026ndash;0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.460\u0026ndash;0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETR score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u0026ndash;0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAUC: area under the curve, CI: confidential interval, PPV: positive predictive value, NPV: negative predictive value, SII: systemic immune-inflammation index, NLR: neutrophil to lymphocyte ratio, MLR: monocyte to lymphocyte ratio, PLR: platelet to lymphocyte ratio, GPS: Glasgow prognostic score, ETR score: endometrial cancer-tumor related score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the new scoring system\u003c/h2\u003e \u003cp\u003eThe ROC curve showed that the new scoring system was a better prognostic tool than the FIGO staging system for predicting EC recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The cutoff value of the ETR score was calculated as 2.5, using the Youden index. According to this cutoff value, we considered\u0026thinsp;\u0026gt;\u0026thinsp;3 points as the poor group, with a sensitivity of 63.9% and a specificity of 84.0% for diagnosing recurrence. In addition, we used this cutoff point to evaluate the sensitivity and specificity for predicting death. Sensitivity and specificity were 70.0% and 81.0%, respectively. Kaplan\u0026ndash;Meier life table analysis revealed significant differences in disease-free survival and overall survival using a cutoff value of 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Cox regression analyses demonstrated that the ETR score was a significant independent prognostic factor affecting the disease-free survival of patients with EC (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\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\u003eUnivariate and multivariate analyses predicting recurrence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\u003epatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eCox multivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIGO stage\u003c/p\u003e \u003cp\u003e\u0026ge; Ⅲ\u003c/p\u003e \u003cp\u003e\u0026lt; Ⅰ \u0026ndash; Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphovascular invasion\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites cytology\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyometrial invasion\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1/2\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative CEA\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.4\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative\u003c/p\u003e \u003cp\u003ed-dimer\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.1\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative-postoperative WBC (/\u0026micro;L)\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1,050\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETR score\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.127\u0026ndash;19.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eHR: hazard ratio, CI: confidential interval, FIGO: The international federation of gynecology and obstetrics, CEA: carcinoembryonic antigen, WBC: white blood cell, ETR: endometrial cancer-tumor related.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we successfully developed a new scoring system, the ETR score, to predict EC recurrence after radical surgery. The score comprised lymph node metastasis, myometrial invasion, preoperative CEA levels, preoperative D-dimer levels, and WBC difference (pre\u0026ndash;post). The strength of the ETR score is that it is easy to calculate because blood tests and histological findings have already been used worldwide.\u003c/p\u003e \u003cp\u003eSerum CEA, an item of the ETR score, is a diagnostic and prognostic marker of broad-spectrum malignant tumors, especially colon and rectal cancers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Postoperative CEA levels reflect recurrence or distant metastasis of EC [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], suggesting a relationship between EC prognosis and CEA level. In this study, preoperative CEA levels were included in the predictive scoring system for recurrence, suggesting that these values predict recurrence.\u003c/p\u003e \u003cp\u003eThere have been reports linking high WBC counts to aggressive tumors or poor prognosis. According to a large cohort study conducted in the UK Biobank, elevated WBC counts may indicate an overly active inflammatory response, which could contribute to the eventual onset of certain types of cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In endometrial neoplasia, WBC counts were significantly higher in patients with cancer than in those with hyperplasia, according to a study that compared the hyperplasia, EC, and control groups [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Based on a few reports suggesting the usefulness of pretreatment peripheral WBC counts, this difference may help predict the prognosis. Our previous study showed that this difference contributes to the prognosis of ovarian cancer by comparing presurgical and postsurgical analyses [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study reaffirmed the usefulness of the differences in WBC counts in predicting recurrence.\u003c/p\u003e \u003cp\u003eSerum D-dimer level, an item of the ETR score, is a well-known biomarker of thrombosis, such as pulmonary embolism and venous thrombosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, it is also known as a prognostic marker for several malignancies, such as ovarian cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], breast cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], lung cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and other cancers [\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. According to a previous study, it is reasonable to include serum D-dimer levels in the new scoring system.\u003c/p\u003e \u003cp\u003eThis study had a few limitations. First, a bias might exist owing to the nature of a retrospective and single-center study. Second, although serous and clear cell carcinomas have poor prognoses, the study cohort did not show a significant difference in histological types. Such patients ordinarily harbor an advanced stage and then receive chemotherapy rather than surgical treatment. ETR score could not reflect these small pathological groups.\u003c/p\u003e \u003cp\u003eIn conclusion, the ETR score, which is composed of the presence of lymph node metastasis and myometrial invasion, preoperative CEA and D-dimer levels, and the difference in (pre\u0026ndash;post) WBC levels, is a good prognostic marker for patients with EC who have undergone complete surgery. Prospective multicenter studies are warranted to validate our findings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA list of patients with primary, previously untreated, and suspected EC who underwent surgery at the Nara Medical University Hospital between January 2007 and December 2020 was generated from our institutional registry. The patients were followed up until March 2024. Informed consent for the use of the patients’ clinical data for research was obtained from all subjects at the first hospitalization. After approval by the Ethics Review Committee of the Nara Medical Hospital, the opt-out form was provided through our institutional homepage. This study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Institutional Ethics Committee of Nara Medical University Hospital (protocol code: 3603).\u003c/p\u003e \u003cp\u003ePatients with suspected EC were included in this study. The inclusion criteria were as follows: (1) histologically confirmed EC after surgery, (2) lymphadenectomy, and (3) did not undergo chemotherapy or radiotherapy before the first surgery. The exclusion criteria were as follows: (1) stage IV, (2) combined with other malignant tumors or hematologic diseases, (3) lost to follow-up, and (4) insufficient preoperative and postoperative serum data, which included no blood tests within 14–60 days or performed while infection occurred. Written consent for the use of the patient’s clinical data for research was obtained at the first hospitalization, and after approval by the Ethics Review Committee of the Nara Medical Hospital, the opt-out form was provided through our institutional homepage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCollection of candidates predicting recurrence\u003c/h2\u003e \u003cp\u003eThe following data were collected through a chart review of the patients’ medical records: age; body mass index; parity; and postoperative diagnosis, including the FIGO stage, histological type, myometrial invasion, lymphovascular invasion, ascites cytology, lymph node metastasis, and distant metastasis. In addition, data concerning the use of adjuvant chemotherapy and the results of preoperative and postoperative blood tests were collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExamination of prognosis using past prognostic indicators\u003c/h2\u003e \u003cp\u003eWe examined the efficacy of past inflammation-based prognostic indices such as SII, NLR, MLR, PLR, and GPS. These indicators were calculated using the following formulae: SII = platelet count×neutrophil count/lymphocyte count, NLR = neutrophil count/lymphocyte count, MLR = monocyte count/lymphocyte count, and PLR = platelet count/lymphocyte count. If an elevated C-reactive protein level (\u0026gt; 1.0 mg/dL) and hypoalbuminemia (\u0026lt; 3.5 g/dL) were present, the GPS was 2. Patients with only one of these were assigned a score of 1, and those with none were assigned a score of 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS version 29.0 (IBM Corp., Armonk, NY, USA). Differences in each factor were compared using Student’s t-test or the Mann–Whitney U test after assessing whether the distribution was normal. The receiver operating characteristic (ROC) curve analysis was performed to determine the cutoff value for predicting poor prognosis. The cutoff value was based on the highest Youden index (i.e., sensitivity + specificity–1). Next, logistic regression analyses were used to assess the risk factors for recurrence and death. Kaplan–Meier life table analysis and log-rank tests were used to assess the disease-free survival and overall survival rates. A two-sided \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant. Multivariate analysis of prognostic factors for PFS and OS was performed using the Cox proportional hazard regression model.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, T.M.; methodology, T.M. and N.K.; validation, T.M. and N.K.; formal analysis, T.M. and N.K.; investigation, T.M. and N.K.; resources, T.M., N.K., R.K., and F.K.; data curation, T.M., N.K., and S.S.; writing\u0026mdash;original draft preparation, T.M. and N.K.; writing\u0026mdash;review and editing, T.M., N.K., R.K., and F.K.; visualization, T.M., J.K., M.M., and K.W.; supervision, F.K.; project administration, F.K. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCao, M., Li, H., Sun, D. \u0026amp; Chen, W. Cancer burden of major cancers in China: A need for sustainable actions. \u003cem\u003eCancer Commun. (Lond.)\u003c/em\u003e\u003cstrong\u003e40\u003c/strong\u003e, 205\u0026ndash;210 (2020). \u003c/li\u003e\n\u003cli\u003eFerlay, J. \u003cem\u003eet al.\u003c/em\u003e Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. \u003cem\u003eEur. J. Cancer\u003c/em\u003e\u003cstrong\u003e103\u003c/strong\u003e, 356\u0026ndash;387 (2018).\u003c/li\u003e\n\u003cli\u003eBray, F. \u003cem\u003eet al.\u003c/em\u003e Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 coun\u0026shy;tries. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e\u003cstrong\u003e68\u003c/strong\u003e, 394\u0026ndash;424 (2018).\u003c/li\u003e\n\u003cli\u003eCapozzi, V. A. \u003cem\u003eet al.\u003c/em\u003e Pattern of recurrence in endometrial cancer. The murderer always returns to the scene of the crime. \u003cem\u003eEur. J. Surg. Oncol.\u003c/em\u003e\u003cstrong\u003e50\u003c/strong\u003e, 107985 (2024).\u003c/li\u003e\n\u003cli\u003eSabater, S. \u003cem\u003eet al.\u003c/em\u003e Does postoperative irradiation improve survival in early-stage endometrial cancer? \u003cem\u003eBrachytherapy\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 912\u0026ndash;921 (2018).\u003c/li\u003e\n\u003cli\u003eBerek, J. S. \u003cem\u003eet al.\u003c/em\u003e FIGO staging of endometrial cancer: 2023. \u003cem\u003eJ. Gynecol. Oncol.\u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e, e85 (2023).\u003c/li\u003e\n\u003cli\u003eAmant, F. \u003cem\u003eet al.\u003c/em\u003e Endometrial cancer. \u003cem\u003eLancet\u003c/em\u003e\u003cstrong\u003e366\u003c/strong\u003e, 491\u0026ndash;505 (2005).\u003c/li\u003e\n\u003cli\u003eBrooks, R. A. \u003cem\u003eet al.\u003c/em\u003e Current recommendations and recent progress in endometrial cancer. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e\u003cstrong\u003e69\u003c/strong\u003e, 258\u0026ndash;279 (2019).\u003c/li\u003e\n\u003cli\u003eCrosbie, E. J. \u003cem\u003eet al.\u003c/em\u003e Endometrial cancer. \u003cem\u003eLancet\u003c/em\u003e\u003cstrong\u003e399\u003c/strong\u003e, 1412\u0026ndash;1428 (2022).\u003c/li\u003e\n\u003cli\u003eChan, J. K. \u003cem\u003eet al.\u003c/em\u003e Therapeutic role of lymph node resection in endometrioid corpus cancer: a study of 12,333 patients. \u003cem\u003eCancer\u003c/em\u003e\u003cstrong\u003e107\u003c/strong\u003e, 1823\u0026ndash;1830 (2006).\u003c/li\u003e\n\u003cli\u003eTodo, Y. \u003cem\u003eet al.\u003c/em\u003e Survival effect of para-aortic lymphadenectomy in endometrial cancer (SEPAL study): a retrospective cohort analysis. \u003cem\u003eLancet\u003c/em\u003e\u003cstrong\u003e375\u003c/strong\u003e, 1165\u0026ndash;1172 (2010).\u003c/li\u003e\n\u003cli\u003eMatsubara, S., Mabuchi, S., Takeda, Y., Kawahara, N. \u0026amp; Kobayashi, H. Prognostic value of pre-treatment systemic immune-inflammation index in patients with endometrial cancer. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, e0248871 (2021).\u003c/li\u003e\n\u003cli\u003eN\u0026oslash;st, T. H. \u003cem\u003eet al.\u003c/em\u003e Systemic inflammation markers and cancer incidence in the UK Biobank. \u003cem\u003eEur. J. Epidemiol.\u003c/em\u003e\u003cstrong\u003e36\u003c/strong\u003e, 841\u0026ndash;848 (2021).\u003c/li\u003e\n\u003cli\u003eLeng, J., Wu, F. \u0026amp; Zhang, L. Prognostic Significance of Pretreatment Neutrophil-to- Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, or Monocyte-to-Lymphocyte Ratio in Endometrial Neoplasms: A Systematic Review and Meta-analysis. \u003cem\u003eFront. Oncol.\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 734948 (2022).\u003c/li\u003e\n\u003cli\u003eZhu, J., Wang, H., Liu, C. C., Lu, Y. \u0026amp; Tang, H. The Glasgow Prognostic Score (GPS) is a novel prognostic indicator in advanced epithelial ovarian cancer: a multicenter retrospective study. \u003cem\u003eJ. Cancer Res. Clin. Oncol.\u003c/em\u003e\u003cstrong\u003e142\u003c/strong\u003e, 2339\u0026ndash;2345 (2016).\u003c/li\u003e\n\u003cli\u003eKawahara, N., Miyake, R., Yamanaka, S. \u0026amp; Kobayashi, H. A Novel Predictive Tool for Discriminating Endometriosis Associated Ovarian Cancer from Ovarian Endometrioma: The R2 Predictive Index. \u003cem\u003eCancers (Basel)\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 3829 (2021).\u003c/li\u003e\n\u003cli\u003eMizuno, H. \u003cem\u003eet al.\u003c/em\u003e Optimal cutoff value of preoperative CEA and CA19-9 for prognostic significance in patients with stage II/III colon cancer. \u003cem\u003eLangenbecks Arch. Surg.\u003c/em\u003e\u003cstrong\u003e406\u003c/strong\u003e, 1987\u0026ndash;1997 (2021).\u003c/li\u003e\n\u003cli\u003eKozakiewicz, B., Chądzyńska, M., Dmoch-Gajzlerska, E. \u0026amp; Stefaniak, M. Monitoring the treatment outcome in endometrial cancer patients by CEA and TATI. \u003cem\u003eTumour Biol.\u003c/em\u003e\u003cstrong\u003e37\u003c/strong\u003e, 9367\u0026ndash;9374 (2016).\u003c/li\u003e\n\u003cli\u003eSong, M., Graubard, B. I., Loftfield, E., Rabkin, C. S. \u0026amp; Engels, E. A. White Blood Cell Count, Neutrophil-to-Lymphocyte Ratio, and Incident Cancer in the UK Biobank. \u003cem\u003eCancer Epidemiol. Biomarkers Prev.\u003c/em\u003e\u003cstrong\u003e33\u003c/strong\u003e, 821\u0026ndash;829 (2024).\u003c/li\u003e\n\u003cli\u003eAcmaz, G. \u003cem\u003eet al.\u003c/em\u003e Are neutrophil/lymphocyte and platelet/lymphocyte ratios associated with endometrial precancerous and cancerous lesions in patients with abnormal uterine bleeding? \u003cem\u003eAsian Pac. J. Cancer Prev.\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 1689\u0026ndash;1692 (2014).\u003c/li\u003e\n\u003cli\u003eKawahara, N. \u003cem\u003eet al.\u003c/em\u003e The prognosis predictive score around primary debulking surgery (PPSP) improves diagnostic efficacy in predicting the prognosis of ovarian cancer. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 22636 (2022).\u003c/li\u003e\n\u003cli\u003eKawahara, N. \u003cem\u003eet al.\u003c/em\u003e The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients. \u003cem\u003eCancers (Basel)\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 5062 (2023).\u003c/li\u003e\n\u003cli\u003evan Es, N. \u003cem\u003eet al.\u003c/em\u003e Diagnostic management of acute pulmonary embolism: a prediction model based on a patient data meta-analysis. \u003cem\u003eEur. Heart J.\u003c/em\u003e\u003cstrong\u003e44\u003c/strong\u003e, 3073\u0026ndash;3081 (2023).\u003c/li\u003e\n\u003cli\u003eYamada, Y. \u003cem\u003eet al.\u003c/em\u003e Preoperative plasma D-dimer level is a useful prognostic marker in ovarian cancer. \u003cem\u003eJ. Obstet. Gynaecol.\u003c/em\u003e\u003cstrong\u003e40\u003c/strong\u003e, 102\u0026ndash;106 (2020).\u003c/li\u003e\n\u003cli\u003eBatschauer, A. P. B. \u003cem\u003eet al.\u003c/em\u003e D-dimer as a possible prognostic marker of operable hormone receptor-negative breast cancer. \u003cem\u003eAnn. Oncol.\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 1267\u0026ndash;1272 (2010).\u003c/li\u003e\n\u003cli\u003eChen, C. \u003cem\u003eet al.\u003c/em\u003e Plasma D-dimer and interleukin-6 are associated with treatment response and progression-free survival in advanced NSCLC patients on anti-PD-1 therapy. \u003cem\u003eCancer Med.\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 15831\u0026ndash;15840 (2023).\u003c/li\u003e\n\u003cli\u003eStender, M. T., Larsen, T. B., S\u0026oslash;rensen, H. T. \u0026amp; Thorlacius-Ussing, O. Preoperative plasma D-dimer predicts 1-year survival in colorectal cancer patients with absence of venous thromboembolism (VTE): a prospective clinical cohort study. \u003cem\u003eJ. Thromb. Haemost.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 2027\u0026ndash;2031 (2012).\u003c/li\u003e\n\u003cli\u003eDiao, D. \u003cem\u003eet al.\u003c/em\u003e D-dimer: not just an indicator of venous thrombosis but a predictor of asymptomatic hematogenous metastasis in gastric cancer patients. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, e101125 (2014).\u003c/li\u003e\n\u003cli\u003eDiao, D. \u003cem\u003eet al.\u003c/em\u003e D-dimer is an essential accompaniment of circulating tumor cells in gastric cancer. \u003cem\u003eBMC Cancer\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 56 (2017).\u003c/li\u003e\n\u003cli\u003eWang, Y. \u0026amp; Wang, Z. Predictive value of plasma D-dimer levels in patients with advanced non-small-cell lung cancer. \u003cem\u003eOnco Targets Ther.\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 805\u0026ndash;808 (2015).\u003c/li\u003e\n\u003cli\u003eBi, X. W. \u003cem\u003eet al.\u003c/em\u003e High pretreatment D-dimer levels correlate with adverse clinical features and predict poor survival in patients with natural killer/T-cell lymphoma. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, e0152842 (2016).\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":"Endometrial cancer, Recurrence, Disease-free survival rate, ETR score, D-dimer, White blood cell count","lastPublishedDoi":"10.21203/rs.3.rs-4709115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4709115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecently, there have been an increasing number of reports on the association between inflammatory markers and the prognosis of malignant tumors. However, the current indicators have limited accuracy. We aimed to develop a new scoring system for predicting endometrial cancer recurrence using inflammatory markers, tumor markers, and histological diagnosis. Patients with primary, previously untreated, and suspected endometrial cancer who underwent surgery at the Nara Medical University Hospital between January 2007 and December 2020 were included and followed up until March 2024. Items were divided into positive and negative using scores based on cutoff values and placed into the new scoring system, the endometrial tumor-related (ETR) score. We found that positive postoperative histological examination of lymph node metastasis and myometrial invasion, high levels of carcinoembryonic antigen and D-dimer in preoperative blood tests, and a large difference in preoperative and postoperative white blood cell counts were significantly associated with recurrence. The prediction of recurrence using the ETR score was superior to that using the International Federation of Gynecology and Obstetrics staging system, which is considered the best prognostic factor for survival. The ETR score is a significant prognostic marker of recurrence in patients who have undergone lymphadenectomy, with complete surgical tumor removal.\u003c/p\u003e","manuscriptTitle":"Novel prognostic score for endometrial cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 21:11:42","doi":"10.21203/rs.3.rs-4709115/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":"2e64ded9-7d82-473a-b076-f17e7abcce53","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-30T05:36:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 21:11:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4709115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4709115","identity":"rs-4709115","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}