Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma

Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma 君 胡, Xiaobing Jiao, Lirong Zhu, Hongyan Guo, Yumei Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-37395/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background As we all know, patients with epithelial ovarian carcinomahave poor prognosis and high recurrence rate. It is critical and challenging to screen out the patients with high risk of recurrence. At present, there are some models predicting the overall survival of epithelial ovarian carcinoma, however, thereis no widely accepted tool or applicable model predicting the recurrence risk of epithelial ovarian carcinomapatients. The objective of this study was to establish and verify a nomogram to predict the recurrence risk of EOC. Results The nomogram for 3-year recurrence risk was established with FIGO stage, histological grade, histological type, lymph node metastasis status and serum CA125 level at diagnosis. The total score can be obtained by adding the grading values of these factors together. In the external validation, the AUC (C statistics) was 0.803 [95%CI, 0.738-0.867] and the Chi-square value is 11.04 (P=0.135>0.05). With the threshold value of 198, the sensitivity, specificity, positive predictive value, negative predictive value and correct index of the monogram were 75.7%, 77.0%, 83.2%, 67.9%, and 0.52 respectively. Conclusions We established and validated a nomogram to predict 3-year recurrence risk of patients with EOC who achieved clinical complete remission after cytoreductive surgery and chemotherapy. This nomogram with good discrimination and calibration might be useful for screening out the patients with high risk of recurrence. Sexual & Reproductive Medicine Ovarian epithelial carcinoma Recurrence free interval Recurrence risk Nomograms Verification Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Epithelial ovarian carcinoma is a common gynecological malignancy. 75% of the cases were diagnosed as advanced stage (stage III/IV), and the 5-year survival rate was only 20%–39%[1–2]. Even in the patients who have achieved clinical complete remission (CCR) after active treatment, 25% of the early (I / II) patients with epithelial ovarian cancer and 80% of the late (III / IV) patients with epithelial ovarian cancer will eventually relapse [3–4]. Although patients with primary early-stage ovarian cancer have an overall favorable prognosis, survival after recurrence is poor and comparable to those with recurrent advanced-stage disease [5]. It is critical and challenging to screen out the patients with high risk of recurrence. To predict the recurrence risk of patients with EOC, we need to combine FIGO staging, histological grade, histological type, lymph node metastasis, the size of residual lesions after surgery and other clinicopathological factors, as well as serum carbohydrate antigen 125 (CA125) level, tumor tissue molecular markers and other factors. At present, there is no widely accepted tool or model predicting the recurrence risk of EOC patients. The purpose of this study is to identify the influencing factors of recurrence in patients with epithelial ovarian cancer by retrospective cohort study, and to establish a contour map model for predicting recurrence risk, so as to provide a convenient quantitative standard for clinical treatment of patients with EOC and for judging recurrence risk. Methods The patients diagnosed as EOC were enrolled from Peking University First Hospital, Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital between January 2003 and December 2013. The selection criteria were EOC patients who reached CCR after initial or intermediate cytoreductive surgery and standard adjuvant platinum-based chemotherapy. Patients who received fertility sparing surgery or with history of other malignant tumors were excluded. CCR is defined as: (1) the level of serum CA125 is within the normal range; and (2) no residual lesions are found by imaging examination after primary treatments. Basic information, size of residual lesions, FIGO stage, histological grade, histological type, lymph node metastasis, expression of estrogen receptor (ER), progesterone receptor (PR) and Ki67, adjuvant therapy and serum CA125 level were collected from the original medical records. All the patients were followed up by telephone call and clinical visits. Follow-up was conducted every 2–4 months in the first and second year, every 3–6 months in the third, fourth and fifth year, and every year after 5 years. All patients were followed up until 30 June 2019. The end points of follow-up were recurrence, no recurrence but death, or no recurrence and no death at the end of observation. The definition of recurrence of EOC is that the serum CA125 level is higher than the normal value and/or the recurrence focus is found by imaging examination. Recurrence-free interval (RFI) is defined as the interval between the recurrence and the end of last chemotherapy of first line treatment. The EOC patients who fulfilled the criteria from Peking University First Hospital were enrolled for the training group to establish the monogram. And the medical data of EOC patients with 5-year standard follow-up in Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital were used for external validation of the nomogram. This study was approved by the Ethics Committee of the First Hospital of Peking University. The number of ethical review was No. (2018) Scientific Research No. (109). Kaplan-Meier univariate survival analysis, Log-rank test and Cox univariate and multivariate regression analysis were used to screen out the factors related to recurrence in patients with EOC. Least absolute shrinkage and selection operator regression was used to analyze the related factors. The nomogram was established with the risk factors selected by LASSO regression. Bootstrap resampling, AUC curve and Hosmer-Lemeshow good of fit test were used to evaluate the discrimination and calibration. The software used for statistical analysis includes SPSS 23.0, R 3.5.2 and EmpowerStats. Differences were considered to be significant at P < 0.05. Results 193 EOC patients from Peking University First Hospital were enrolled into the training group. The characteristics of these patients, including age, FIGO stage, histological grade, histological type, lymph node metastasis, residual lesion size, serum CA125 level and molecular markers of tumor tissues were summarized in Table 1. 106 cases (54.9%) had recurrence. The recurrence-free interval (RFI) ranged from 1.8 months to 173.2 months, with a median of 46.7 months. 77 cases had no recurrence; 10 cases had censored data, 9 cases had lost follow-up, 1 case died of other disease, the rate of lost follow-up was 4.7%. The results of Kaplan Meier survival analysis and log rank test are summarized in table 1. Cox regression univariate analysis showed that FIGO staging, histological grade, histological type, size of residual lesions after surgery, lymph node metastasis, pre-treatment CA125 level, ER expression in tumor tissue had significant differences in the impact of internal stratification on recurrence. Cox regression multivariate analysis showed that advanced EOC, histological grade and histological type were independent risk factors for recurrence of epithelial ovarian cancer. The results of specific stratification factor were shown in Table 2. LASSO regression was used to screen the best influencing factors for the establishment of the model. The optimal number of factors used to establish the contour map prediction model was 5. The final selected model included the following 5 variables: FIGO staging, histological grade, histological type, lymph node metastasis and serum CA125 level before treatment. Each stratification factor is assigned with a specific grading value (see Table 3 for details). When the grading values of the five influencing factors are determined, the total score can be obtained by adding them together. Figure 1 showed the nomogram for predicting 3-year recurrence risks of patients with EOC. The mathematical formulas between the total score and the recurrence rate for 3 years are as follows: 3-year recurrence rate = 1 - [1.51e–07 * total score ^ 3 + (–0.000101727) * total score ^ 2 + 0.016191444 * total score + 0.144929485] For example, a patient with EOC had a serum CA125 level of 600U/ml (21 points) underwent the initial cytoreductive surgery. Pathology result showed that she was stage IIIC (65 points), serous carcinoma (26 points), grade G3 (100 points), lymph node metastasis (41 points) and she has reached CCR after 6 cycles of standardized chemotherapy. According to the above-mentioned contour map model, the total score of the patient was 253. The relatively overall 3-year predicted recurrence rate for this patient was 82.01%. The ROC curve of the monogram was shown in Figure 2. The Area under ROC curve (C statistics) was 0.828 (95% CI, 0.764–0.884). When the threshold value was set at 198, the sensitivity, specificity, positive predictive value, negative predictive value and correct index were 88.8%, 67.0%, 71.8%, 86.3% and 0.558 respectively. Patients with total score higher than 198 were identified with high-risk recurrence and those with total score lower than 198 were identified with low-risk recurrence. Hosmer-Lemeshow test for evaluation of calibration showed that the Chi-square value is 3.6 (P = 0.731>0.05)， As the calibration curve shown in Figure 3, if the 3-year predicted recurrence rate calculated by the model is within the range of 15% to 30%, the predicted value is basically consistent with the actual recurrence rate; if the predicted value is below 15% or above 30%, the predicted value is less than the actual recurrence rate, indicating that the recurrence risk is underestimated. The medical data of 187 EOC patients from in Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital were enrolled into the external validation group. The ROC curve of the contour map model was shown in Figure 4. The AUC (C statistics) for the validation data group was 0.803 (95% CI, 0.738–0.867). When using the threshold value of 198, the sensitivity, specificity, positive predictive value, negative predictive value and correct index were 75.7%, 77.0%, 83.2%, 67.9%, and 0.52 respectively. Hosmer-Lemeshow test for evaluation of calibration showed that the Chi-square value is11.074 (P = 0.135>0.05)， Discussion There are some literature reports on the survival prediction model of patients with EOC [6–10], while the recurrence prediction model of patients with EOC is relatively less [11]. In this study, the influencing factors related to the recurrence of EOC were screened out and evaluated by mathematical methods. And a predictive monogram model of 3-year recurrence risk was established and verified externally. Comparison between the observed and expected responses suggests that this predicting model has good discrimination and calibration. Many studies have confirmed that FIGO staging, histological grade, histological type, size of residual lesions, lymph node metastasis, serum CA125 level before treatment are associated with recurrence of EOC [11–14, 15,16,]. In our study, patients with advanced stage, serous carcinoma, high grade, lymph node metastasis and high serum CA125 level before treatment had relatively shorter RFI, which was consistent with the literature. Although Cox regression analysis confirmed that patients with no residual tumor had shorter RFI (P<0.001) than the other patients, the LASSO regression didn’t put it into the monogram model. This might be related to the inclusion criteria that all the patients should reach the status of CCR and only 10% of the patients had residual lesion size bigger than 1cm, which may decrease the effect of residual lesion size on the recurrence risk. The relationship between ER/PR expression and recurrence of epithelial ovarian cancer is controversial. A total of 2933 patients with epithelial ovarian cancer were included in Sieh’s study. It was found that ER-positive patients had a better prognosis in endometrioid cancer, while ER-positive patients in serous, mucinous and clear cell carcinomas had no significant correlation with prognosis. PR-positive patients in endometrioid and high-grade ovarian serous carcinomas had a better prognosis, while there was no significant correlation between PR positive expression and prognosis in patients with low-grade ovarian serous, mucinous carcinomas and clear cell carcinomas [17]. A meta-analysis of 35 studies showed that the disease-free survival (DFS) of patients with ER-positive EOC was better than that of patients with ER-negative EOC [18]. Therefore, the relationship between ER/PR expression and recurrence of ovarian cancer is not clear, and there are inconsistent conclusions among various studies. In our study, Cox regression analysis confirmed that patients with ER positive expression in tumor tissue had shorter RFI (HR, 1.713; 95% CI, 1.057–2.776; P = 0.029) than those with negative expression. However, given the literature review and high P value, we didn’t put this factor into the monogram model. At present, most of the studies related to the recurrence of EOC are still limited to obtaining Cox proportional risk model, which was complex and not convenient for clinical application [12–14]. In this study, the Cox proportional hazard model is transformed into a more intuitive and easy-to-calculate contour diagram model by using mathematical method and R software. When using this monogram to predict the 3-year recurrence rate of EOC patients, the internal and external AUC (C statistics) obtained from ROC curve were 0.828 (95% CI, 0.764–0.884) and 0.803 (95% CI, 0.738–0.867) respectively, indicating that the model had a good distinction. However, from the calibration curve, it can be seen that although the actual red curve and the ideal black curve are not very different, the area divided by the blue curve representing 95% CI does not completely contain the ideal black curve, which shows that the calibration degree of the model is general. This may also be related to the small sample size, resulting in greater fluctuation of the predicted value. Therefore, it is still necessary to increase the sample size and further adjust the model parameters to achieve better calibration in the future. According to the RFI distribution of EOC patients, most relapsed patients will relapse within three years after primary treatment [1–5]. So in this study we aimed to stratify the 3-year recurrence risk of EOC patients by using the predictive monogram model, and to individualize the treatment and follow-up plan according to the risk stratification. The result of external validation ensured the transportability and generalizability of the monogram. For EOC patients with high recurrence risk, aggressive maintenance therapy with targeted drugs, endocrinal therapy or immune drugs after chemotherapy may help to reduce the recurrence risk of such patients and improve their prognosis. And for patients at low risk of recurrence, we may reduce the frequency of follow-up appropriately and make individualized follow-up plan to lower the expenses in the first 3 years. However, our study still had some limitation. This study is a retrospective cohort study and all the patients reached a status of CCR after primary treatment, which may both lead to the selection bias. The influencing factors included are traditional clinicopathological factors, molecular markers (such as serum human epididymis protein 4, BRAC gene detection), targeted therapy, endocrine therapy, immunotherapy were not included in the scope of this study. The external verification results of the model indicates that a larger sampling and abundant is needed for model establishment to ensure a better discriminative and calibration power. Prospective randomized controlled trials are still needed to prove the feasibility of layering treatment and follow-up plans according to recurrence risk. Conclusions The monogram constructed by FIGO staging, histological grade, histological type, lymph node metastasis and serum CA125 level before treatment could be used to predict the 3-year recurrence risk of patients who reach CCR after primary treatment. This nomogram with good discrimination and calibration might be useful for screening out the patients with high risk of recurrence. Declarations Ethical approval This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.This study was approved by the Ethics Committee of the First Hospital of Peking University. The number of ethical review was No. (2018) Scientific Research No. (109). Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors had no funding. Author contributions JH conceived the study, participated in study setup, study design, data retrieval and analysis, data management and the manuscript. XJ participated in data analysis and manuscript editing. LZ conceived the study, participated in study setup, study design. HG protocol development, data collection, study design. All authors have read and approved the final manuscript. YW protocol development, data collection. All authors have read and approved the final manuscript. All authors read and approved the final manuscript. Acknowledgments We sincerely thank Xiaobing Jiao, M. D., Department of Gynecology and Obstetrics, Peking University First Hospital, for his technical support of statistical analysis. References 1 Zeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, et al. Changing cancer survival in China during 2003–15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health, 2018;6: e555–67. 2 Armstrong DK, Bundy B, Wenzel L, Huang HQ, Baergen R, Lele S, et al. Intraperitoneal cisplatin and paclitaxel in ovarian cancer. N Engl J Med, 2006;354: 34–43. 3 Trimbos JB, Parmar M, Vergote I, Guthrie D, Bolis G, Colombo N, et al. International Collaborative Ovarian Neoplasm trial 1 and Adjuvant ChemoTherapy In Ovarian Neoplasm trial: two parallel randomized phase III trials of adjuvant chemotherapy in patients with early-stage ovarian carcinoma. J Natl Cancer Inst, 2003;95: 105–12. 4 Ledermann J A, Raja F A, Fotopoulou C, Gonzalez-Martin A, Colombo N, Sessa C, et al. Newly diagnosed and relapsed epithelial ovarian carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2013;24 Suppl 6: vi24–32. 5 Chan JK, Tian C, Teoh D, Monk BJ, Herzog T, Kapp DS, et al.Survival after recurrence in early-stage high-risk epithelial ovarian cancer: A Gynecological Oncology Group Study. Gynecologic Oncology, 2010;116:307–11 6 Polcher M, Friedrichs N, Rudlowski C, Fimmer R, Keyver- Paik MD, Kübler K, et al.Changes in Ki–67 labeling indices during neoadjuvant chemotherapy for advanced ovarian cancer are associated with survival. Int J Gynecol Cancer, 2010;20: 555–60. 7 Kim H, Kim K, No J H, Jeon YT, Jeon HW, Kim YB. Prognostic value of biomarkers related to drug resistance in patients with advanced epithelial ovarian cancer. Anticancer Res, 2012;32: 589–94. 8 Chen M, Yao S, Cao Q, Xia M, Liu J, He M. The prognostic value of Ki67 in ovarian high-grade serous carcinoma: an 11-year cohort study of Chinese patients. Oncotarget, 2017;8: 107877–85. 9 Garcia-Velasco A, Mendiola C, Sanchez-Munoz A, Ballestín C, Colomer R, Cortés-Funes H. Prognostic value of hormonal receptors, p53, ki67 and HER2/neu expression in epithelial ovarian carcinoma. Clin Transl Oncol, 2008;10: 367–71. 10 Ditto A , Leone Roberti Maggiore U , Bogani G , Martinelli F, Chiappa V, Evangelista MT,et al. Predictive factors of recurrence in patients with early-stage epithelial ovarian cancer. Int J Gynaecol Obstet. 2019;14:28–33. 11 Kajiyama H, Mizuno M, Shibata K, Yamamoto E, Kawai M, Nagasaka T, et al. Recurrence-predicting prognostic factors for patients with early-stage epithelial ovarian cancer undergoing fertility-sparing surgery: a multi-institutional study. Eur J Obstet Gynecol Reprod Biol, 2014;175: 97–102. 12 Karagol H, Saip P, Eralp Y, Topuz S, Berkman S, Ilhan R, et al. Factors related to recurrence after pathological complete response to postoperative chemotherapy in patients with epithelial ovarian cancer. Tumori, 2009;95:207–11. 13 Chang C, Chiang A J, Chen W A, et al. A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence. Biomark Med,2016; 10: 53–61. 14 Rizzuto I, Stavraka C, Chatterjee J, Borley J, Hopkins TG, Gabra H, et al. Risk of Ovarian Cancer Relapse score: a prognostic algorithm to predict relapse following treatment for advanced ovarian cancer. Int J Gynecol Cancer, 2015;25: 416–22. 15 Lenhard S M, Bufe A, Kumper C, Stieber P, Mayr D, Hertlein L, et al. Relapse and survival in early-stage ovarian cancer. Arch Gynecol Obstet, 2009;280: 71–7. 16 Yang Z J, Zhao B B, Li L. The significance of the change pattern of serum CA125 level for judging prognosis and diagnosing recurrences of epithelial ovarian cancer. J Ovarian Res, 2016;9: 57. 17 Sieh W, Kobel M, Longacre T A, Bowtell DD, deFazio A, Goodman MT, et al. Hormone-receptor expression and ovarian cancer survival: an Ovarian Tumor Tissue Analysis consortium study. Lancet Oncol, 2013;14: 853–62. 18 Zhao D, Zhang F, Zhang W, He J, Zhao Y, Sun J, et al. Prognostic role of hormone receptors in ovarian cancer: a systematic review and meta-analysis. Int J Gynecol Cancer, 2013;23: 25–33. Tables Table 1 Kaplan-Meier single factor survival analysis of patients in training group Factors Stratification factor Number(%) Median RFI(months) P value Age ≤50 years old 68(35.2%) 53.0 0.778 >50years old 125(64.8%) 48.0 FIGO stage I 55(28.5%) NA <0.001 II 26(13.5%) NA III 92(47.7%) 18.0 IV 20(10.4%) 10.1 Histological grade G1 37(19.2%) NA <0.001 G2 45(23.3%) 27.6 G3 111(57.5%) 26.4 Histological type Serous carcinoma 117(60.6%) 24.0 <0.001 Non-serous carcinoma＊ 76(39.4%) NA Postoperative residual size 0 78(40.4%) NA <0.001 <1cm 88(45.6%) 26.8 ≥1cm 27(14.0%) 18.0 Lymph node status No metastasis 53(27.5%) NA <0.001 Metastasis 17(8.8%) 10.1 No Lymphonectomy 123(63.7%) 27.3 Pretreatment CA125 level <35U/mL 30(15.5%) NA <0.001 ≥35 and <1000 U/mL 118(61.1%) 53.0 ≥1000 U/mL 45(23.3%) 16.1 Expression of ER in tumor tissues Negative 70(36.3%) NA 0.008 Positive 123(63.7%) 32.5 Expression of PR in tumor tissues Negative 81(42.0%) 27.6 0.192 Positive 112(58.0%) 94.5 Non-serous cancers include endometrioid, clear cell, mucinous, undifferentiated and mixed epithelial tumors. NA: Not available. Table 2 The result of Cox multi-regression survival analysis Factors Stratification factor HR 95%CI P FIGO stage I 1 II 2.3 0.8-6.4 0.102 III 5.9 2.1-16.4 0.001 IV 6.3 2.0-20.0 0.002 Histological grade G1 1 G2 6.4 1.4-28.4 0.015 G3 6.9 1.6-31.1 0.011 Histological type Serous carcinoma 1 Non-serous carcinoma ＊ 1.8 1.1-2.9 0.027 Postoperative residual size 0 1 <1cm 0.6 0.3-1.1 0.099 ≥1cm 0.7 0.4-1.5 0.392 Lymph node status No metastasis 1 Metastasis 2.0 0.8-4.8 0.114 Not available 1.6 0.8-3.3 0.158 Pretreatment CA125 level <35U/mL 1 ≥35 and <1000 U/mL 1.7 0.6-5.0 0.304 ≥1000 U/mL 2.0 0.7-6.2 0.210 Expression of ER in tumor tissues Negative 1 Positive 1.0 0.6-1.6 0.942 Table 3 Scores for Recurrence related Factors Recurrence related Factors Stratification factor Score FIGO stage I 0 II 29 III 65 IV 71 Histological grade G1 0 G2 96 G3 100 Histological type Serous carcinoma 0 Non-serous carcinoma 26 Lymph node status No metastasis 0 Metastasis 41 Not available 27 Pretreatment CA125 level <35U/mL 0 ≥35 and <1000 U/mL 21 ≥1000 U/mL 28 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-37395","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":701824,"identity":"bd884d02-9963-4159-b9a4-c6afbf2b0539","order_by":0,"name":"君 胡","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACAwaGBAYGHhCT+cCBDxWkaWFLPDjjDHFaYIDH+DBvCxFazCUSnkkXyNgk9rf3fDjA28Agzy92AL8WyxkJadIzeNISZ5w5u+GA5A4Gw5mzEwg47AZQCw/P4cQNErkbDhieYUgwuE20Fvk3Dw4ktpGkRYKH4cBBorSceZBszcOTZjzjTJrBwYYzEkT45XhO4m3eHhvZ/vbDjz//qbCR55cmoAUYHQkMjD0Mjg0QngQh5SDAfoCB4QeDPTFKR8EoGAWjYIQCAER0SYphHhDRAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8255-2121","institution":"Peking University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"君","middleName":"","lastName":"胡","suffix":""},{"id":701825,"identity":"77ce1c15-be33-47b6-ae45-340095ca8d0d","order_by":1,"name":"Xiaobing Jiao","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaobing","middleName":"","lastName":"Jiao","suffix":""},{"id":701826,"identity":"1a943bed-25b5-4212-95da-d9f991e55dc4","order_by":2,"name":"Lirong Zhu","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Zhu","suffix":""},{"id":701827,"identity":"db1f94e4-c5ee-4097-a17d-8c4701001cb6","order_by":3,"name":"Hongyan Guo","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Guo","suffix":""},{"id":701828,"identity":"a4a1170a-adf5-4bb5-b708-d75f4a45c7f0","order_by":4,"name":"Yumei Wu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yumei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2020-06-22 05:55:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-37395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-37395/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1392187,"identity":"2b3c105c-cd2a-4d62-a947-5a59201c7902","added_by":"auto","created_at":"2020-06-22 20:13:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74729,"visible":true,"origin":"","legend":"A monogram for predicting 3-year recurrence risk in EOC patients. \nNote: A line perpendicular to and intersecting with the grading coordinate axis is drawn upward from the position of the grading factors of each influencing factor coordinate axis. When the grading values of the five influencing factors are determined, the total score can be obtained by adding them together. Draw a line perpendicular to and intersecting with the coordinate axis of predicting recurrence rate from the position of total score. The intersection point is the 3-year predicted recurrence rate related to the total score.\n","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-37395/v1/Figure1.png"},{"id":1392188,"identity":"6daa1c37-330b-4305-ac1a-7425797a5d83","added_by":"auto","created_at":"2020-06-22 20:13:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49423,"visible":true,"origin":"","legend":"ROC curve of the monogram with the training group.","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-37395/v1/Figure2.png"},{"id":1392189,"identity":"93fad133-f49c-4105-a150-fbccd048fa24","added_by":"auto","created_at":"2020-06-22 20:13:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83264,"visible":true,"origin":"","legend":"The calibration curve of the monogram.\nNote: The horizontal coordinate axis of the chart is a 3-year predicted recurrence rate, and the vertical coordinate axis is a 3-year actual recurrence rate. The red curve is a calibration curve which corresponds to the actual recurrence rate. The blue curve represents the 95% CI range of the calibration curve. The black line is an ideal calibration when the 3-year predicted recurrence rate is equal to the actual recurrence rate. ","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-37395/v1/Figure3.png"},{"id":1392190,"identity":"1d4d829d-ebd3-44c2-ad04-299387cf211d","added_by":"auto","created_at":"2020-06-22 20:13:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106541,"visible":true,"origin":"","legend":"ROC curve of the monogram with the external verification group.","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-37395/v1/figure4.png"},{"id":13543846,"identity":"0fd6ab94-914f-4f0f-b711-43c455cfca02","added_by":"auto","created_at":"2021-09-17 01:59:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":406414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-37395/v1/86c10e7c-8590-4d04-8707-baa4b29cdd92.pdf"}],"financialInterests":"","formattedTitle":"Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEpithelial ovarian carcinoma is a common gynecological malignancy. 75% of the cases were diagnosed as advanced stage (stage III/IV), and the 5-year survival rate was only 20%–39%[1–2]. Even in the patients who have achieved clinical complete remission (CCR) after active treatment, 25% of the early (I / II) patients with epithelial ovarian cancer and 80% of the late (III / IV) patients with epithelial ovarian cancer will eventually relapse [3–4]. Although patients with primary early-stage ovarian cancer have an overall favorable prognosis, survival after recurrence is poor and comparable to those with recurrent advanced-stage disease [5]. It is critical and challenging to screen out the patients with high risk of recurrence. To predict the recurrence risk of patients with EOC, we need to combine FIGO staging, histological grade, histological type, lymph node metastasis, the size of residual lesions after surgery and other clinicopathological factors, as well as serum carbohydrate antigen 125 (CA125) level, tumor tissue molecular markers and other factors. At present, there is no widely accepted tool or model predicting the recurrence risk of EOC patients. The purpose of this study is to identify the influencing factors of recurrence in patients with epithelial ovarian cancer by retrospective cohort study, and to establish a contour map model for predicting recurrence risk, so as to provide a convenient quantitative standard for clinical treatment of patients with EOC and for judging recurrence risk.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe patients diagnosed as EOC were enrolled from Peking University First Hospital, Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital between January 2003 and December 2013. The selection criteria were EOC patients who reached CCR after initial or intermediate cytoreductive surgery and standard adjuvant platinum-based chemotherapy. Patients who received fertility sparing surgery or with history of other malignant tumors were excluded. CCR is defined as: (1) the level of serum CA125 is within the normal range; and (2) no residual lesions are found by imaging examination after primary treatments. Basic information, size of residual lesions, FIGO stage, histological grade, histological type, lymph node metastasis, expression of estrogen receptor (ER), progesterone receptor (PR) and Ki67, adjuvant therapy and serum CA125 level were collected from the original medical records. All the patients were followed up by telephone call and clinical visits. Follow-up was conducted every 2–4 months in the first and second year, every 3–6 months in the third, fourth and fifth year, and every year after 5 years. All patients were followed up until 30 June 2019. The end points of follow-up were recurrence, no recurrence but death, or no recurrence and no death at the end of observation. The definition of recurrence of EOC is that the serum CA125 level is higher than the normal value and/or the recurrence focus is found by imaging examination. Recurrence-free interval (RFI) is defined as the interval between the recurrence and the end of last chemotherapy of first line treatment. The EOC patients who fulfilled the criteria from Peking University First Hospital were enrolled for the training group to establish the monogram. And the medical data of EOC patients with 5-year standard follow-up in Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital were used for external validation of the nomogram. This study was approved by the Ethics Committee of the First Hospital of Peking University. The number of ethical review was No. (2018) Scientific Research No. (109).\u003c/p\u003e\n\u003cp\u003eKaplan-Meier univariate survival analysis, Log-rank test and Cox univariate and multivariate regression analysis were used to screen out the factors related to recurrence in patients with EOC. Least absolute shrinkage and selection operator regression was used to analyze the related factors. The nomogram was established with the risk factors selected by LASSO regression. Bootstrap resampling, AUC curve and Hosmer-Lemeshow good of fit test were used to evaluate the discrimination and calibration. The software used for statistical analysis includes SPSS 23.0, R 3.5.2 and EmpowerStats. Differences were considered to be significant at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e193 EOC patients from Peking University First Hospital were enrolled into the training group. The characteristics of these patients, including age, FIGO stage, histological grade, histological type, lymph node metastasis, residual lesion size, serum CA125 level and molecular markers of tumor tissues were summarized in Table 1. 106 cases (54.9%) had recurrence. The recurrence-free interval (RFI) ranged from 1.8 months to 173.2 months, with a median of 46.7 months. 77 cases had no recurrence; 10 cases had censored data, 9 cases had lost follow-up, 1 case died of other disease, the rate of lost follow-up was 4.7%. The results of Kaplan Meier survival analysis and log rank test are summarized in table 1.\u003c/p\u003e\n\u003cp\u003eCox regression univariate analysis showed that FIGO staging, histological grade, histological type, size of residual lesions after surgery, lymph node metastasis, pre-treatment CA125 level, ER expression in tumor tissue had significant differences in the impact of internal stratification on recurrence. Cox regression multivariate analysis showed that advanced EOC, histological grade and histological type were independent risk factors for recurrence of epithelial ovarian cancer. The results of specific stratification factor were shown in Table 2.\u003c/p\u003e\n\u003cp\u003eLASSO regression was used to screen the best influencing factors for the establishment of the model. The optimal number of factors used to establish the contour map prediction model was 5. The final selected model included the following 5 variables: FIGO staging, histological grade, histological type, lymph node metastasis and serum CA125 level before treatment. Each stratification factor is assigned with a specific grading value (see Table 3 for details). When the grading values of the five influencing factors are determined, the total score can be obtained by adding them together. Figure 1 showed the nomogram for predicting 3-year recurrence risks of patients with EOC. The mathematical formulas between the total score and the recurrence rate for 3 years are as follows:\u003c/p\u003e\n\u003cp\u003e3-year recurrence rate = 1 - [1.51e–07 * total score ^ 3 + (–0.000101727) * total score ^ 2 + 0.016191444 * total score + 0.144929485]\u003c/p\u003e\n\u003cp\u003eFor example, a patient with EOC had a serum CA125 level of 600U/ml (21 points) underwent the initial cytoreductive surgery. Pathology result showed that she was stage IIIC (65 points), serous carcinoma (26 points), grade G3 (100 points), lymph node metastasis (41 points) and she has reached CCR after 6 cycles of standardized chemotherapy. According to the above-mentioned contour map model, the total score of the patient was 253. The relatively overall 3-year predicted recurrence rate for this patient was 82.01%.\u003c/p\u003e\n\u003cp\u003eThe ROC curve of the monogram was shown in Figure 2. The Area under ROC curve (C statistics) was 0.828 (95% CI, 0.764–0.884). When the threshold value was set at 198, the sensitivity, specificity, positive predictive value, negative predictive value and correct index were 88.8%, 67.0%, 71.8%, 86.3% and 0.558 respectively. Patients with total score higher than 198 were identified with high-risk recurrence and those with total score lower than 198 were identified with low-risk recurrence. Hosmer-Lemeshow test for evaluation of calibration showed that the Chi-square value is 3.6 (P = 0.731\u0026gt;0.05)， As the calibration curve shown in Figure 3, if the 3-year predicted recurrence rate calculated by the model is within the range of 15% to 30%, the predicted value is basically consistent with the actual recurrence rate; if the predicted value is below 15% or above 30%, the predicted value is less than the actual recurrence rate, indicating that the recurrence risk is underestimated.\u003c/p\u003e\n\u003cp\u003eThe medical data of 187 EOC patients from in Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital were enrolled into the external validation group. The ROC curve of the contour map model was shown in Figure 4. The AUC (C statistics) for the validation data group was 0.803 (95% CI, 0.738–0.867). When using the threshold value of 198, the sensitivity, specificity, positive predictive value, negative predictive value and correct index were 75.7%, 77.0%, 83.2%, 67.9%, and 0.52 respectively. Hosmer-Lemeshow test for evaluation of calibration showed that the Chi-square value is11.074 (P = 0.135\u0026gt;0.05)，\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere are some literature reports on the survival prediction model of patients with EOC [6–10], while the recurrence prediction model of patients with EOC is relatively less [11]. In this study, the influencing factors related to the recurrence of EOC were screened out and evaluated by mathematical methods. And a predictive monogram model of 3-year recurrence risk was established and verified externally. Comparison between the observed and expected responses suggests that this predicting model has good discrimination and calibration.\u003c/p\u003e\n\u003cp\u003eMany studies have confirmed that FIGO staging, histological grade, histological type, size of residual lesions, lymph node metastasis, serum CA125 level before treatment are associated with recurrence of EOC [11–14, 15,16,]. In our study, patients with advanced stage, serous carcinoma, high grade, lymph node metastasis and high serum CA125 level before treatment had relatively shorter RFI, which was consistent with the literature. Although Cox regression analysis confirmed that patients with no residual tumor had shorter RFI (P\u0026lt;0.001) than the other patients, the LASSO regression didn’t put it into the monogram model. This might be related to the inclusion criteria that all the patients should reach the status of CCR and only 10% of the patients had residual lesion size bigger than 1cm, which may decrease the effect of residual lesion size on the recurrence risk.\u003c/p\u003e\n\u003cp\u003eThe relationship between ER/PR expression and recurrence of epithelial ovarian cancer is controversial. A total of 2933 patients with epithelial ovarian cancer were included in Sieh’s study. It was found that ER-positive patients had a better prognosis in endometrioid cancer, while ER-positive patients in serous, mucinous and clear cell carcinomas had no significant correlation with prognosis. PR-positive patients in endometrioid and high-grade ovarian serous carcinomas had a better prognosis, while there was no significant correlation between PR positive expression and prognosis in patients with low-grade ovarian serous, mucinous carcinomas and clear cell carcinomas [17]. A meta-analysis of 35 studies showed that the disease-free survival (DFS) of patients with ER-positive EOC was better than that of patients with ER-negative EOC [18]. Therefore, the relationship between ER/PR expression and recurrence of ovarian cancer is not clear, and there are inconsistent conclusions among various studies. In our study, Cox regression analysis confirmed that patients with ER positive expression in tumor tissue had shorter RFI (HR, 1.713; 95% CI, 1.057–2.776; P = 0.029) than those with negative expression. However, given the literature review and high P value, we didn’t put this factor into the monogram model.\u003c/p\u003e\n\u003cp\u003eAt present, most of the studies related to the recurrence of EOC are still limited to obtaining Cox proportional risk model, which was complex and not convenient for clinical application [12–14]. In this study, the Cox proportional hazard model is transformed into a more intuitive and easy-to-calculate contour diagram model by using mathematical method and R software. When using this monogram to predict the 3-year recurrence rate of EOC patients, the internal and external AUC (C statistics) obtained from ROC curve were 0.828 (95% CI, 0.764–0.884) and 0.803 (95% CI, 0.738–0.867) respectively, indicating that the model had a good distinction. However, from the calibration curve, it can be seen that although the actual red curve and the ideal black curve are not very different, the area divided by the blue curve representing 95% CI does not completely contain the ideal black curve, which shows that the calibration degree of the model is general. This may also be related to the small sample size, resulting in greater fluctuation of the predicted value. Therefore, it is still necessary to increase the sample size and further adjust the model parameters to achieve better calibration in the future.\u003c/p\u003e\n\u003cp\u003eAccording to the RFI distribution of EOC patients, most relapsed patients will relapse within three years after primary treatment [1–5]. So in this study we aimed to stratify the 3-year recurrence risk of EOC patients by using the predictive monogram model, and to individualize the treatment and follow-up plan according to the risk stratification. The result of external validation ensured the transportability and generalizability of the monogram. For EOC patients with high recurrence risk, aggressive maintenance therapy with targeted drugs, endocrinal therapy or immune drugs after chemotherapy may help to reduce the recurrence risk of such patients and improve their prognosis. And for patients at low risk of recurrence, we may reduce the frequency of follow-up appropriately and make individualized follow-up plan to lower the expenses in the first 3 years.\u003c/p\u003e\n\u003cp\u003eHowever, our study still had some limitation. This study is a retrospective cohort study and all the patients reached a status of CCR after primary treatment, which may both lead to the selection bias. The influencing factors included are traditional clinicopathological factors, molecular markers (such as serum human epididymis protein 4, BRAC gene detection), targeted therapy, endocrine therapy, immunotherapy were not included in the scope of this study. The external verification results of the model indicates that a larger sampling and abundant is needed for model establishment to ensure a better discriminative and calibration power. Prospective randomized controlled trials are still needed to prove the feasibility of layering treatment and follow-up plans according to recurrence risk.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe monogram constructed by FIGO staging, histological grade, histological type, lymph node metastasis and serum CA125 level before treatment could be used to predict the 3-year recurrence risk of patients who reach CCR after primary treatment. This nomogram with good discrimination and calibration might be useful for screening out the patients with high risk of recurrence.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical approval\u003c/em\u003e This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.This study was approved by the Ethics Committee of the First Hospital of Peking University. The number of ethical review was No. (2018) Scientific Research No. (109).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to participate\u003c/em\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003e\u003cem\u003eConsent to publication \u003c/em\u003e Not applicable\u003c/p\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003e\u003cem\u003eAvailability of data and materials \u003c/em\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003e\u003cem\u003eFunding \u003c/em\u003eThe authors had no funding.\u003c/p\u003e\n\u003ch3 data-xsweet-outline-level=\"0\"\u003eAuthor contributions\u003c/h3\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003eJH conceived the study, participated in study setup, study design, data retrieval and analysis, data management and the manuscript.\u003c/p\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003eXJ participated in data analysis and manuscript editing.\u003c/p\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003eLZ conceived the study, participated in study setup, study design.\u003c/p\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003eHG protocol development, data collection, study design. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp data-xsweet-outline-level=\"0\"\u003eYW protocol development, data collection. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments \u003c/em\u003eWe sincerely thank Xiaobing Jiao, M. D., Department of Gynecology and Obstetrics, Peking University First Hospital, for his technical support of statistical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1 Zeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, et al. Changing cancer survival in China during 2003–15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health, 2018;6: e555–67.\u003c/p\u003e\n\u003cp\u003e2 Armstrong DK, Bundy B, Wenzel L, Huang HQ, Baergen R, Lele S, et al. Intraperitoneal cisplatin and paclitaxel in ovarian cancer. N Engl J Med, 2006;354: 34–43.\u003c/p\u003e\n\u003cp\u003e3 Trimbos JB, Parmar M, Vergote I, Guthrie D, Bolis G, Colombo N, et al. International Collaborative Ovarian Neoplasm trial 1 and Adjuvant ChemoTherapy In Ovarian Neoplasm trial: two parallel randomized phase III trials of adjuvant chemotherapy in patients with early-stage ovarian carcinoma. J Natl Cancer Inst, 2003;95: 105–12.\u003c/p\u003e\n\u003cp\u003e4 Ledermann J A, Raja F A, Fotopoulou C, Gonzalez-Martin A, Colombo N, Sessa C, et al. Newly diagnosed and relapsed epithelial ovarian carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2013;24 Suppl 6: vi24–32.\u003c/p\u003e\n\u003cp\u003e5 Chan JK, Tian C, Teoh D, Monk BJ, Herzog T, Kapp DS, et al.Survival after recurrence in early-stage high-risk epithelial ovarian cancer: A Gynecological Oncology Group Study. Gynecologic Oncology, 2010;116:307–11\u003c/p\u003e\n\u003cp\u003e6 Polcher M, Friedrichs N, Rudlowski C, Fimmer R, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Keyver-Paik+MD\u0026amp;cauthor_id=20442588\"\u003eKeyver-\u003c/a\u003e \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Keyver-Paik+MD\u0026amp;cauthor_id=20442588\"\u003ePaik\u003c/a\u003e MD, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=K%C3%BCbler+K\u0026amp;cauthor_id=20442588\"\u003eKübler\u003c/a\u003e K, et al.Changes in Ki–67 labeling indices during neoadjuvant chemotherapy for advanced ovarian cancer are associated with survival. Int J Gynecol Cancer, 2010;20: 555–60.\u003c/p\u003e\n\u003cp\u003e7 Kim H, Kim K, No J H, Jeon YT, Jeon HW, Kim YB. Prognostic value of biomarkers related to drug resistance in patients with advanced epithelial ovarian cancer. Anticancer Res, 2012;32: 589–94.\u003c/p\u003e\n\u003cp\u003e8 Chen M, Yao S, Cao Q, Xia M, Liu J, He M. The prognostic value of Ki67 in ovarian high-grade serous carcinoma: an 11-year cohort study of Chinese patients. Oncotarget, 2017;8: 107877–85.\u003c/p\u003e\n\u003cp\u003e9 Garcia-Velasco A, Mendiola C, Sanchez-Munoz A, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Ballest%C3%ADn+C\u0026amp;cauthor_id=18558584\"\u003eBallestín\u003c/a\u003e C, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Colomer+R\u0026amp;cauthor_id=18558584\"\u003eColomer\u003c/a\u003e R, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Cort%C3%A9s-Funes+H\u0026amp;cauthor_id=18558584\"\u003eCortés-Funes\u003c/a\u003e H. Prognostic value of hormonal receptors, p53, ki67 and HER2/neu expression in epithelial ovarian carcinoma. Clin Transl Oncol, 2008;10: 367–71.\u003c/p\u003e\n\u003cp\u003e10 \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Ditto%20A%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30698271\"\u003eDitto A\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Leone%20Roberti%20Maggiore%20U%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30698271\"\u003eLeone Roberti Maggiore U\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Bogani%20G%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30698271\"\u003eBogani G\u003c/a\u003e, Martinelli F, Chiappa V, Evangelista MT,et al. Predictive factors of recurrence in patients with early-stage epithelial ovarian cancer. Int J Gynaecol Obstet. 2019;14:28–33.\u003c/p\u003e\n\u003cp\u003e11 Kajiyama H, Mizuno M, Shibata K, Yamamoto E, Kawai M, Nagasaka T, et al. Recurrence-predicting prognostic factors for patients with early-stage epithelial ovarian cancer undergoing fertility-sparing surgery: a multi-institutional study. Eur J Obstet Gynecol Reprod Biol, 2014;175: 97–102.\u003c/p\u003e\n\u003cp\u003e12 Karagol H, Saip P, Eralp Y, Topuz S, Berkman S, Ilhan R, et al. Factors related to recurrence after pathological complete response to postoperative chemotherapy in patients with epithelial ovarian cancer. Tumori, 2009;95:207–11.\u003c/p\u003e\n\u003cp\u003e13 Chang C, Chiang A J, Chen W A, et al. A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence. Biomark Med,2016; 10: 53–61.\u003c/p\u003e\n\u003cp\u003e14 Rizzuto I, Stavraka C, Chatterjee J, Borley J, Hopkins TG, Gabra H, et al. Risk of Ovarian Cancer Relapse score: a prognostic algorithm to predict relapse following treatment for advanced ovarian cancer. Int J Gynecol Cancer, 2015;25: 416–22.\u003c/p\u003e\n\u003cp\u003e15 Lenhard S M, Bufe A, Kumper C, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Stieber+P\u0026amp;cauthor_id=19093129\"\u003eStieber\u003c/a\u003e P, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Mayr+D\u0026amp;cauthor_id=19093129\"\u003e Mayr\u003c/a\u003e D, \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Hertlein+L\u0026amp;cauthor_id=19093129\"\u003e Hertlein\u003c/a\u003e L, et al. Relapse and survival in early-stage ovarian cancer. Arch Gynecol Obstet, 2009;280: 71–7.\u003c/p\u003e\n\u003cp\u003e16 Yang Z J, Zhao B B, Li L. The significance of the change pattern of serum CA125 level for judging prognosis and diagnosing recurrences of epithelial ovarian cancer. J Ovarian Res, 2016;9: 57.\u003c/p\u003e\n\u003cp\u003e17 Sieh W, Kobel M, Longacre T A, Bowtell DD, deFazio A, Goodman MT, et al. Hormone-receptor expression and ovarian cancer survival: an Ovarian Tumor Tissue Analysis consortium study. Lancet Oncol, 2013;14: 853–62.\u003c/p\u003e\n\u003cp\u003e18 Zhao D, Zhang F, Zhang W, He J, Zhao Y, Sun J, et al. Prognostic role of hormone receptors in ovarian cancer: a systematic review and meta-analysis. Int J Gynecol Cancer, 2013;23: 25–33.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Kaplan-Meier single factor survival analysis of patients in training group\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eFactors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eStratification factor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNumber(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eMedian RFI(months)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026le;50 years old\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e68(35.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e53.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003e0.778\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026gt;50years old\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e125(64.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e48.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\"\u003e\n\u003cp\u003eFIGO stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e55(28.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e26(13.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e92(47.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e18.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e20(10.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e10.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eHistological grade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e37(19.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e45(23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e27.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e111(57.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e26.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eHistological type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eSerous carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e117(60.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e24.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eNon-serous carcinoma＊\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e76(39.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003ePostoperative residual size\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e78(40.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026lt;1cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e88(45.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026ge;1cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e27(14.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e18.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eLymph node status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNo metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e53(27.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eMetastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e17(8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e10.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eNo Lymphonectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e123(63.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e27.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003ePretreatment CA125 level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026lt;35U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e30(15.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026ge;35 and \u0026lt;1000 U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e118(61.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e53.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026ge;1000 U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e45(23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e16.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eExpression of ER in tumor tissues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e70(36.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e123(63.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e32.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eExpression of PR in tumor tissues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e81(42.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e27.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003e0.192\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e112(58.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e94.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003eNon-serous cancers include endometrioid, clear cell, mucinous, undifferentiated and mixed epithelial tumors.\u003c/li\u003e\n\u003cli\u003eNA: Not available.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 The result of Cox multi-regression survival analysis\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eFactors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eStratification factor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\"\u003e\n\u003cp\u003eFIGO stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.8-6.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.1-16.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e6.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.0-20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eHistological grade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e6.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.4-28.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.6-31.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eHistological type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eSerous carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eNon-serous carcinoma\u003csup\u003e＊\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.1-2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003ePostoperative residual size\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026lt;1cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.3-1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026ge;1cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.4-1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.392\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eLymph node status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNo metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eMetastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.8-4.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.114\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eNot available\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.8-3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.158\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003ePretreatment CA125 level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026lt;35U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026ge;35 and \u0026lt;1000 U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.6-5.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.304\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026ge;1000 U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.7-6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.210\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eExpression of ER in tumor tissues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.6-1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.942\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Scores for Recurrence related Factors\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eRecurrence related Factors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eStratification factor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003eScore\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 136px;\" rowspan=\"4\" width=\"37%\"\u003e\n\u003cp\u003eFIGO stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 102px;\" rowspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003eHistological grade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34.0001px;\"\u003e\n\u003ctd style=\"height: 34.0001px;\" width=\"37%\"\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34.0001px;\" width=\"25%\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 68px;\" rowspan=\"2\" width=\"37%\"\u003e\n\u003cp\u003eHistological type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eSerous carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eNon-serous carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 102px;\" rowspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003eLymph node status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eNo metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eMetastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003eNot available\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 102px;\" rowspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003ePretreatment CA125 level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003e\u0026lt;35U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003e\u0026ge;35 and \u0026lt;1000 U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 34px;\"\u003e\n\u003ctd style=\"height: 34px;\" width=\"37%\"\u003e\n\u003cp\u003e\u0026ge;1000 U/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 34px;\" width=\"25%\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Ovarian epithelial carcinoma, Recurrence free interval, Recurrence risk, Nomograms, Verification","lastPublishedDoi":"10.21203/rs.3.rs-37395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-37395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cp\u003eAs we all know, patients with epithelial ovarian carcinomahave poor prognosis and high recurrence rate. It is critical and challenging to screen out the patients with high risk of recurrence. At present, there are some models predicting the overall survival of\u0026nbsp;epithelial ovarian carcinoma, however, thereis no widely accepted tool or applicable model predicting the recurrence risk of\u0026nbsp;epithelial ovarian carcinomapatients. The objective of this study was to establish and verify a nomogram to predict the recurrence risk of EOC.\u003c/p\u003e\u003cp\u003eResults\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe nomogram for 3-year recurrence risk was established with FIGO stage, histological grade, histological type, lymph node metastasis status and serum CA125 level at diagnosis. The total score can be obtained by adding the grading values of these factors together. In the external validation, the AUC (C statistics) was 0.803 [95%CI, 0.738-0.867] and the Chi-square value is 11.04 (P=0.135\u0026gt;0.05). With the threshold value of 198, the sensitivity, specificity, positive predictive value, negative predictive value and correct index of the monogram were 75.7%, 77.0%, 83.2%, 67.9%, and 0.52 respectively.\u003c/p\u003e\u003cp\u003eConclusions\u0026nbsp;\u003c/p\u003e\u003cp\u003eWe established and validated a nomogram to predict 3-year recurrence risk of patients with EOC who achieved clinical complete remission after cytoreductive surgery and chemotherapy. This nomogram with good discrimination and calibration might be useful for screening out the patients with high risk of recurrence.\u003c/p\u003e","manuscriptTitle":"Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-06-22 20:13:29","doi":"10.21203/rs.3.rs-37395/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":"899da6c8-caba-4bd6-8d04-af24f99d950c","owner":[],"postedDate":"June 22nd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":127944,"name":"Sexual \u0026 Reproductive Medicine"}],"tags":[],"updatedAt":"2020-06-30T21:39:23+00:00","versionOfRecord":[],"versionCreatedAt":"2020-06-22 20:13:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-37395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-37395","identity":"rs-37395","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}