A Novel Model To Predict Endometriosis in Patients with Ovarian Cysts: A Retrospective Study.

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Junmiao Xiang, Wei Shen, Zongwen Liang, Qiong Zhang, Ping Duan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-762131/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 Objective To develop a model that uses hematological indexes and clinical characteristics to help estimate the probability of endometriosis in patients with ovarian cysts. Methods A retrospective study was conducted on 2242 patients who underwent surgery for benign ovarian cysts from January 2008 to November 2016. Variables included in the model were serum tumor markers, blood routine test, age, BMI, reproductive history, history of hysteroscopy, menstrual episodes. Logistic regression was used to construct a predictive model for endometriosis, Receiver Operating Characteristic curves and the areas under the curve was used to verify the model’s validities. Ten-fold cross-validation was primarily used as an internal validation to evaluate the prediction accuracies of the model. Normalized mean square errors (NMSE) was obtained to compare the reliability of different models. Results 978 (43.6%) patients with endometriosis were included in the strudy. Univariate analysis showed that age, BMI, delivery, dysmenorrhea, menstrual cycle, duration of menstrual flow, history of hysteroscopy, CA-125 and CA-19-9 ( P < 0.001) are associated with endometriosis. The area under the receiver operating characteristic curve for the model with CA-125 alone was 0.888, with a sensitivity of 81.6% and specificity of 83.5%. After adjustment for other multiple covariates, including age, mature delivery, irregular menstruation, dysmenorrhea, menstrual period, history of hysteroscopy, CEA, CA-19-9, monocyte count, platelet count, the model obtained had an AUC of 0.916, with a sensitivity of 0.849 and specificity of 0.864. Conclusions The diagnostic prediction model can be used as a framework for potential improvement in diagnosis of endometriosis in patient with ovarian cyst. Sexual & Reproductive Medicine Cancer Biology Endometriosis ovarian cyst CA-125 CA-19-9 logistic regression modeling Figures Figure 1 Figure 2 Introduction Endometriosis is one of the most common benign gynecological disorders occurring in 6 to 10% of the general female population[ 1 ]. It is defined as the development of endometrial tissue (gland and stroma) outside the uterus such as, ovaries, pelvic peritoneum, and rectovaginal space. Endometriosis is a recurring persistent disease that causes non-menstrual pelvic pain, dyspareunia infertility, dysmenorrhea, and menstrual irregularities [ 2 ]. It is reported as a disease of complex multifactorial etiology, among all the hypothesis, transplantation of endometrial tissues via retrograde menstruation is widely accepted [ 3 ]. The correlation between symptoms and lesions is quite incomprehensible because the symptoms are nonspecific and not diagnostic.[ 4 ]. The diagnosis of endometriosis is based on clinical manifestations and imaging techniques [ 5 ] but confirm diagnosis of the disease can only be obtain by invasive procedures like direct visualization of peritoneal and ovarian implants by laparoscopy or laparotomy followed by histological analysis [ 6 ]. To increase the accuracy of the diagnosis of endometriosis, especially to avoid the use of invasive management, some investigators have begun to characterize the factors contributing to the detection of endometriosis. There is evidence that family history, immunological, menstrual and reproductive factors and are associated with endometriosis[ 7 ], different combinations of these biomarkers, are studied to increase the diagnostic accuracy of this disease.[ 8 – 10 ] The development of multiple factors to improve the accuracy of diagnosis of endometriosis is necessary. The novel model was synthesized by correlating patient’s hematological indexes and clinical characteristics in a multivariate regression model which could help us to recognize which ovarian cysts are more likely to be biopsied so as endometriosis can be diagnosed in early stage. Materials And Methods Patients Retrospective data were collected from The Second Affiliated Hospital of Wenzhou Medical University. A total of 2242 premenopausal women who underwent either laparoscopic or laparotomic surgery in the Gynecology Department of our hospital from January 2008 to November 2016 were included in this study. Patients who underwent surgery for ovarian endometriosis, which was confirmed by surgical specimen histopathological examinations were eligible for our study. The exclusion criteria as follows: history of hormonal therapy for endometriosis, pregnant woman, abnormal hepatic and renal function tests, pelvic inflammatory disease, pathologically confirmed or clinically diagnosed with leiomyoma or adenomyosis, acute infection or history of chronic inflammatory disease, immune system diseases, or malignancy. Data Collection The data were obtained by reviewing the patients’ medical records. All patients underwent routine preoperative laboratory studies, including CA-125, CA-19-9, carcinoembryonic antigen (CEA) and a complete blood count test was performed prior to surgery. Patient-related factors assessed included endometriosis-related symptoms, age, body mass index (BMI), reproductive history (mature delivery, premature delivery, abortion), menstrual history (menstrual cycle, duration of menstrual flow), surgical features (history of laparotomy, laparoscopy or hysteroscopy) and histopathology diagnosis following surgery were retrieved for each patient. Most patients in the study had preoperative ultrasonographic evaluations. The reasons why we do not bring in the ultrasonographic diagnosis of cysts in the study were that sonographic evaluations were performed by different sonographers, transducers and types of ultrasounds. Statistical Analysis Patients characteristics were compared by using variance analysis (t-test, for continuous variables), chi-square test (for dichotomous variables) or Kruskal-Wallis test (for continuous variables in skewed distribution) [ 11 ]. Logistic regression was used to perform multivariate analysis, and forward method was used to select variables[ 12 ]. 10-fold cross validation was used to predict the accuracy of the internal validation model. We randomly divided the data set into 10 copies, with 9 of them as a training set, used to establish the forecast model, the remaining 1 data set as a validation set, as a validation set. Training set to build the model, with the validation set to predict, this process was continued for 10 times and ultimately obtained a complete set of predictions. The predicted value was used to establish a cross-validated ROC curve. At the same time, 10 models were established, and 10 normalized mean square errors (NMSE) were obtained for the verification set and the average NMSE of the model was obtained to compare the reliability of the model. The smaller the NMSE, the more reliable the model is. Delong method was used to compare the significant difference between ROC curves, in which P < 0.05 indicated the difference was statistically significant. The Hosmer-Lemeshow goodness-of-fit test was used to test the predictive ability (calibration) of the model[ 13 ]. The model was evaluated with different indicators, including sensitivity, specificity, area under curve (AUC), and Youden Index, where the signature = sensitivity − (1-specificity). SPSS 13.0 for statistical analysis, R 3.2.3 software pROC package to do the Receiver Operating Curve (ROC) and ROC curve comparison. Bilateral test P < 0.05 showed statistically significant. Results Patients Characteristics: A total of 2242 women were enrolled in the study and the age ranges from 18 to 46 years, with a mean age of 31.37 years. 978 patients had endometriosis while the rest were 860 patients with mature teratoma,145 patients with serous cystadenomas, 183 patients with mucinous cystadenomas, and 76 patients with other benign conditions. The characteristics included baseline demographic, clinical, surgical, and laboratory features of patients with and without endometriosis (Table 1 ). Table 1 Comparison of Characteristics Between Endometriosis with Benign Ovarian Cysts Variables Non-endometriosis (n = 1264) Endometriosis (n = 978) Statistic P-value Age (years) 30.94 ± 6.816 31.92 ± 5.381 t=-3.709 < 0.001 BMI(kg/m2) 21.48 ± 2.982 21.01 ± 2.902 t = 3.701 < 0.001 Mature delivery(n) 1 (0–5) 1 (0–5) Z=-4.459 < 0.001 Premature delivery(n) 0 (0–2) 0 (0–1) Z=-1.306 0.192 Abortion (n) 1 (0–12) 1 (0–10) Z=-0.222 0.824 Menstrual cycle(days) 30.77 ± 5.553 29.54 ± 3.234 t = 6.156 < 0.001 Duration of menstrual flow (days) 5.39 ± 1.395 5.81 ± 1.534 t=-6.860 < 0.001 Irregular menstruation 128 (10.1%) 35 (3.6%) χ2 = 35.056 < 0.001 Dysmenorrhea 254 (20.1%) 358 (36.6%) χ2 = 75.735 < 0.001 Hyperlipidemia 87 (6.9%) 55 (5.6%) χ2 = 1.474 0.256 History of caesarean 204 (16.1%) 181 (18.5%) χ2 = 2.174 0.143 History of laparotomy 375 (29.7%) 253 (25.9%) χ2 = 3.946 0.052 History of laparoscopy 35 (2.8%) 35 (3.6%) χ2 = 1.195 0.274 History of hysteroscopy 4 (0.3%) 25 (2.6%) χ2 = 21.665 < 0.001 CEA(U/mL) 1.28 (0-95.32) 1.12 (0-4.28) Z=-7.096 < 0.001 CA-125(U/mL) 15.54 (3.10-469.10) 45.35 (4.80-660.40) Z=-31.622 < 0.001 CA-19-9(U/mL) 19.13 (0-1147.23) 31.84 (0-1128.52) Z=-6.854 < 0.001 Neutrophil count(X10 3 /mm 3 ) 3.96 (1.16–19.96) 3.86 (1.25–14.40) Z=-0.246 0.806 Lymphocyte count(X10 3 /mm 3 ) 1.83 (0.31–4.45) 1.76 (0.25–4.69) Z=-3.253 0.001 NLR 2.17 (0.56–31.26) 2.22 (0.65–21.98) Z=-1.762 0.078 monocyte count (X10 3 /mm 3 ) 0.42 (0.09–1.46) 0.42 (0.11–1.50) Z=-1.758 0.079 Platelet count (X103/mm 3 ) 211.00 (72.00-516.00) 213.00 (61.00-418.00) Z=-0.785 0.443 MPV 10.70 (6.90–14.50) 10.70 (6.40–14.40) Z=-0.812 0.417 Hemoglobin, g/dL 120.00 (62.00-156.00) 117.00 (65.00-149.00) Z=-5.849 < 0.001 MCV 88.45 (56.80-103.40) 88.30 (60.20–99.20) Z=-1.146 0.252 MCH 29.80 (18.00-35.50) 29.60 (18.20–34.40) Z=-3.337 0.001 MCHC 335.00 (280.00-368.00) 333.00 (288.00-363.00) Z=-4.097 < 0.001 Abbreviations: BMI, body mass index; MPV, mean platelet volume; NLR, neutrophil-to-lymphocyte ratio; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; P < 0.05 is considered as statistically significant. Clinicopathological Features With Endometriosis: After analysis of endometriosis with clinicopathological features, we found that there was no statistical significance with premature delivery (P = 0.192), abortion (P = 0.824), hyperlipidemia (P = 0.256), history of caesarean (P = 0.143), history of laparotomy (P = 0.143), history of laparoscopy (P = 0.143), Neutrophil count (P = 0.806), NLR (P = 0.078), monocyte count (P = 0.079), Platelet count (P = 0.443), MPV (P = 0.417) and MCV (P = 0.252). Further analysis showed that age, BMI, mature delivery, menstrual cycles, duration of menstrual flow, irregular menstruation, dysmenorrhea, history of hysteroscopy, CEA, CA 125, CA-19-9, hemoglobin, MCHC (P < 0.001), lymphocyte count (P = 0.001) and MCH (P = 0.001) were statistically associated with endometriosis (Table 1 ). Univariate And Multivariate Analysis: We selected 16 variables to establish the multivariate logistic regression analysis. As a result, 11 variables were retained in the final logistic regression model (Table 2 ), it revealed that Log(CA-125), dysmenorrhea, history of hysteroscopy, age and mature delivery remained as significant variables associated with endometriosis. The Hosmer–Lemeshow test results revealed an adequate goodness-of-fit for the regression model ( P > 0.05). The association between characteristics and diagnosis of endometriosis was explored and shown in (Table 3 ), all the models (model 1 ~ model 5) were significantly associated with endometriosis. A ROC curve for the model with CA-125 alone was constructed (Fig. 1), the area under the curve was 0.888 (P < 0.001) for the model, with a sensitivity of 0.816% and specificity of 0.835%, These results indicate a moderate predictive performance of the model,after adjustment for other multiple covariates, the result presented a little rise of accuracy in diagnosis of endometriosis. The modle5 of included other values (combined clinical characteristics and haematological indexes) in the CA-125 based probabilistic model showed an AUC of 0.916 ( P ༜001), a sensitivity of 0.849 and specificity of 0.864 (Fig. 2), The Hosmer– Lemeshow test (p = 0.060) in the combined model5 indicate a good fitness of the model characteristic (dysmenorrhea, Irregular menstruation,), laboratory characteristics (Platelet count, monocyte count, CA-19-9, CA-125, CEA). Table 2 Multivariate logistic regression model of morbidity risk in endometriosis. Variables β S.E. Wals OR 95% CI P- Value Age 0.126 0.013 87.396 1.134 1.105–1.165 < 0.001 mature delivery -0.647 0.110 34.303 0.524 0.422–0.650 < 0.001 Irregular menstruation 0.955 0.276 11.941 2.600 1.512–4.469 < 0.001 Dysmenorrhea 0.569 0.142 16.051 1.767 1.337–2.334 < 0.001 Menstrual period 0.259 0.047 30.897 1.296 1.183–1.420 < 0.001 History of hysteroscopy 2.017 0.696 8.395 7.518 1.921–29.430 < 0.001 CEA -0.878 0.104 71.874 0.415 0.339–0.509 < 0.001 CA-19-9 -0.003 0.001 12.340 0.997 0.996–0.999 < 0.001 monocyte count -1.386 0.383 13.116 0.250 0.118–0.530 < 0.001 Platelet count -0.003 0.001 5.351 0.997 0.995–1.000 < 0.001 Log(CA-125) 7.059 0.307 528.6 1162.7 637–2122.4 Constant -13.619 0.803 287.9 0.000 Table 3 Logistic regression models fitting results of the association between non-endometriosis and endometriosis Models H-L test Z P -value AUC Sensitivity Specificity Youden index NMSE Model 1 a < 0.001 7.167 < 0.001 0.888 0.816 0.835 0.651 0.556 Model 2 b < 0.001 6.666 < 0.001 0.891 0.843 0.816 0.659 0.545 Model 3 c < 0.001 6.320 < 0.001 0.899 0.836 0.833 0.669 0.511 Model 4 d < 0.001 4.191 < 0.001 0.911 0.885 0.813 0.698 0.477 Model 5 e 0.060 Reference Reference 0.919 0.849 0.864 0.713 0.441 a Only CA125 in the model, b variables in model 1 plus age, c variables in model 2 plus reproductive history, d variables in model 3 plus mature delivery, history of hysteroscopy, clinical characteristic, e variables in model 4, except CA125 was replaced by log(CA125) Discussion Endometriosis can only be diagnosed by invasive procedures such as laparoscopic or laparotomy exploration. We constructed a non-invasive predictive model based on medical history and hematological indexes (blood routine, serum tumor markers examination) that can diagnose endometriosis in ovarian cyst patients. We found association between the CA-125, CA-19-9, age, partus matures, menstrual episodes, history of hysteroscopy, dysmenorrhea and blood routine test with endometriosis, but no single characteristic predicted endometriosis with a high accuracy. Our study supported the retrograde menstruation theory because the history of hysteroscopy is shown to be associated with an increase in risk of developing endometriosis. Our study confirmed the belief that an increased frequency of and duration of menstruations is associated with endometriosis [ 14 , 15 ]. Dysmenorrhea was the main symptom of endometriosis infertile women (46.92%) with endometriosis and the mechanism of dysmenorrhea in endometriosis lie in increased production of prostaglandins (PGs)[ 16 ]. Moreover, BMI showed a negative correlation with the presence of endometriosis, as was reported previously[ 17 ]. Obesity is often associated with long menstrual cycles, a factor that reduce the risk of endometriosis. It is considered that the reduction of the frequency of menstrual episodes counterbalances the relative hyperestrogenism of women[ 18 ]. Endometriosis is rare before the menarche and tends to decrease after the menopause. Studies conducted in women under age of 45 years suggested that the frequency of endometriosis increases with age until menopause[ 19 ]. While Fuldeore, M. J, et al [ 2 ] reported that the average age of women with endometriosis in their study was 37.8 years compared to 33.8 years women without endometriosis (p < 0.0001), it is possible that incidence of endometriosis increases as women age increases which can be because of the hormonal changes that occur during peri-menopause[ 20 ]. Screening for the diagnosis of patients with clinical suspicion of endometriosis is based on serum CA-125 which have been confirmed in many studies. Shen, A et al [ 21 ] reported that endometriosis is significantly associated with elevated serum CA-125 concentrations, confirmed CA-125 as an auxiliary biological marker in endometriosis diagnosis. Some studies[ 22 , 23 ] did not agree with this finding and showed that the diagnosis of endometriosis on CA-125 alone is not accurate, mainly in relation to their sensitivity, Hirsch, M, et al[ 23 ] reported that CA-125 with a cut-off of ≥ 30 u/ml has a sensitivity of 0.57, which did not meet the criteria for a triage test, and international guidelines do not recommend CA-125 testing in women with suspected endometriosis[ 24 ]. However, in the study we find model 1 which consisted of CA-125 alone predicted endometriosis with high sensitivity (81.6%) and predicted the absence of endometriosis with a specificity of 83.5%. Nevertheless the timing of blood collection for CA-125 is uncontrolled because it’s a retrospective design, the relationship with the menstrual cycle is known to affect this test[ 25 ]. The study shows an inverse association between the number of mature delivery and endometriosis, but no association between the number of abortions and endometriosis has been found. This has also been observed in many studies of endometriosis[ 14 , 26 ]. Parazzini, F, et al [ 26 ]reported that the risk of endometriosis decreased with increasing number of births, compared with nulliparous women, the OR of endometriosis at stage 1 was 0.1 (95% CI 0.1, 0.2) in women reporting two or more births was respectively 0.1 (95% CI 0.1, 0.3), 0.2 (95% CI 0.1, 0.4). It has been reported that [ 27 ] reproductive history may influence hormonal milieu, Estradiol levels is higher among nulliparous women than among parous women, whereas androgen levels have an opposite effect, and reproductive history may influence the volume of endometrial cells released into the peritoneal cavity. The other studies revealed that CA-19-9 can be used to discriminate between patients with or without endometriosis, and it is correlation to severity of the disease, their results showed that CA-19-9 was significantly associated with advanced stage (stage III and IV) endometriosis[ 10 ]. Our results is in concordance with a former study that the mean levels of CA-19-9 are significantly elevated compared with the control group. Endometriosis is associated with increased inflammatory activity which is an important stimulant for platelets[ 28 ], suggest platelet indices is an important and effortless hematological parameter that can be useful in evaluation of endometriosis[ 29 , 30 ]. Evsen, M. S et al reported that platelets count in patients with peritoneal endometriosis were found to be higher from the control group (p = 0.038)[ 30 ], particularly more apparent in advanced stage peritoneal endometriosis. Monocytes also was also implicated as prognostic factor of inflammatory response but there is no evidence supporting that monocyte count is associated with endometriosis, our study shows that it is a protective factor for endometriosis, further study should be done to confirm this finding. This model provides guidance about confirmation of endometriosis. CA-125 can be useful in directing the diagnosis of the disease, and clinical history, tumor marker and routine blood tests increase the diagnosis of endometriosis more accurately. For instance, a peri-menopause woman with multiple reproductive history and irregular menstruation, has a higher chance of containing endometriosis, and if CA-125 is quite high, ovarian cysts would be appropriate to confirm the presence of the disease. Conclusion In this study, we found that CA-125, clinical history, tumor marker and routine blood term testing are predictors of endometriosis. Our model can contribute in diagnosis as a predictor for endometriosis in patients with ovarian cysts. List Of Abbreviations Abbreviation Full name CEA carcinoembryonic antigen BMI body mass index NMSE normalized mean square errors AUC area under curve ROC receiver operating curve Declarations Ethics approval and consent to participate Ethics approval was obtained from Second Affiliated Hospital of Wenzhou Medical University Ethics Committee, and informed consent was obtained from individual participants prior to surgery. Consent for publication Not applicable Availability of data and material The datasets used or analysed during the current study are available from the corresponding author on reasonable request. Competing interests We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Funding No external funding was used in this conduct of this study. Authors' contributions Junmiao Xiang contributed to the conception of the study. Ping Duan contributed significantly to analysis and manuscript preparation. Wei Shen performed the data analyses. Zongwen Liang and Qiong Zhang helped perform the analysis with constructive discussions. Acknowledgements I would like to express my gratitude to all those who helped me during the writing of this paper. My deepest gratitude goes first and foremost to Professor Ping Duan, my supervisor, for her constant encouragement and guidance. And the author gratefully acknowledge the support of the Second Affiliated Hospital of Wenzhou Medical. References Giudice LC, Kao LC. Endometriosis. The Lancet. 2004;364(9447):1789–99. Fuldeore MJ, Soliman AM. Prevalence and Symptomatic Burden of Diagnosed Endometriosis in the United States: National Estimates from a Cross-Sectional Survey of 59,411 Women. Gynecol Obstet Invest. 2016. Halme J, Hammond MG, Hulka JF, Raj SG, Talbert LM. Retrograde menstruation in healthy women and in patients with endometriosis. Obstet Gynecol. 1984;64(2):151–4. 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Reproductive history and endometriosis among premenopausal women. Obstet Gynecol. 2004;104(5 Pt 1):965–74. Bullon P, Navarro JM. Inflammasome as a key pathogenic mechanism in endometriosis. Curr Drug Targets. 2017;18(9):997–1002. Avcioglu SN, Altinkaya SO, Kucuk M, Demircan-Sezer S, Yuksel H. Can platelet indices be new biomarkers for severe endometriosis? ISRN Obstet Gynecol. 2014;2014:713542. Evsen MS, Soydinc HE, Sak ME, Ozler A, Turgut A, Celik Y, et al. Increased platelet count in severe peritoneal endometriosis. Clin Exp Obstet Gynecol. 2014;41(4):423–5. 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. 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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-762131","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":44911086,"identity":"fee6d2d3-798f-47bf-848c-dd05e5a91734","order_by":0,"name":"Junmiao Xiang","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junmiao","middleName":"","lastName":"Xiang","suffix":""},{"id":44911087,"identity":"dece6749-f34e-4ba6-809f-f02c295ca541","order_by":1,"name":"Wei Shen","email":"","orcid":"","institution":"Le Jiu Medical","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shen","suffix":""},{"id":44911088,"identity":"898ac973-2c18-4454-8728-1d9437a6e803","order_by":2,"name":"Zongwen Liang","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zongwen","middleName":"","lastName":"Liang","suffix":""},{"id":44911089,"identity":"b957ba80-b515-43c0-959e-34f052e143d4","order_by":3,"name":"Qiong Zhang","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Zhang","suffix":""},{"id":44911090,"identity":"7f1e097e-e223-4e9a-8713-c4689b602fd0","order_by":4,"name":"Ping Duan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFACxsfMPAYScmzs7QeI1cJszNxTYGHMx3MmgQQtZz5UJM6TcDAgToPB+cPMxmcMJNLbJBgSGH5UbCNCy4HDzMk5BhK5bdKNBxh7ztwmrMXsYP/hw2AtMgcSmBnbiNFymJn5sA3QYWwSCQZEajnGzJwsYyCRQLwW+zPMzMZALYZtwEA+SJRfJPsPM0vz/KmTl29vP/jgRwURWlDAARLVj4JRMApGwSjABQD2zjdRrg5AfAAAAABJRU5ErkJggg==","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2021-07-29 05:35:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-762131/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-762131/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12367222,"identity":"e6a14024-a1e4-402f-bf9d-e68da3f29c63","added_by":"auto","created_at":"2021-08-12 13:28:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48176,"visible":true,"origin":"","legend":"ROC curve of the model1.","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-762131/v1/9e1d8bcb176b1a3feedd0c4b.jpg"},{"id":12367223,"identity":"a043f61b-f3e4-478f-b556-99c89de9b318","added_by":"auto","created_at":"2021-08-12 13:28:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44298,"visible":true,"origin":"","legend":"ROC curve of the new model5","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-762131/v1/3970320392420e44bc21dd2f.jpg"},{"id":13709212,"identity":"30ad9aef-dde7-4a11-bfbb-a012765179ed","added_by":"auto","created_at":"2021-09-17 14:11:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":411242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-762131/v1/7802a045-a617-4f9a-8e98-ac76121f550a.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eA Novel Model To Predict Endometriosis in Patients with Ovarian Cysts: A Retrospective Study.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis is one of the most common benign gynecological disorders occurring in 6 to 10% of the general female population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is defined as the development of endometrial tissue (gland and stroma) outside the uterus such as, ovaries, pelvic peritoneum, and rectovaginal space. Endometriosis is a recurring persistent disease that causes non-menstrual pelvic pain, dyspareunia infertility, dysmenorrhea, and menstrual irregularities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is reported as a disease of complex multifactorial etiology, among all the hypothesis, transplantation of endometrial tissues via retrograde menstruation is widely accepted [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The correlation between symptoms and lesions is quite incomprehensible because the symptoms are nonspecific and not diagnostic.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagnosis of endometriosis is based on clinical manifestations and imaging techniques [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] but confirm diagnosis of the disease can only be obtain by invasive procedures like direct visualization of peritoneal and ovarian implants by laparoscopy or laparotomy followed by histological analysis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To increase the accuracy of the diagnosis of endometriosis, especially to avoid the use of invasive management, some investigators have begun to characterize the factors contributing to the detection of endometriosis. There is evidence that family history, immunological, menstrual and reproductive factors and are associated with endometriosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], different combinations of these biomarkers, are studied to increase the diagnostic accuracy of this disease.[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] The development of multiple factors to improve the accuracy of diagnosis of endometriosis is necessary.\u003c/p\u003e \u003cp\u003eThe novel model was synthesized by correlating patient\u0026rsquo;s hematological indexes and clinical characteristics in a multivariate regression model which could help us to recognize which ovarian cysts are more likely to be biopsied so as endometriosis can be diagnosed in early stage.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eRetrospective data were collected from The Second Affiliated Hospital of Wenzhou Medical University. A total of 2242 premenopausal women who underwent either laparoscopic or laparotomic surgery in the Gynecology Department of our hospital from January 2008 to November 2016 were included in this study. Patients who underwent surgery for ovarian endometriosis, which was confirmed by surgical specimen histopathological examinations were eligible for our study. The exclusion criteria as follows: history of hormonal therapy for endometriosis, pregnant woman, abnormal hepatic and renal function tests, pelvic inflammatory disease, pathologically confirmed or clinically diagnosed with leiomyoma or adenomyosis, acute infection or history of chronic inflammatory disease, immune system diseases, or malignancy.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eData Collection\u003c/h2\u003e\n\u003cp\u003e The data were obtained by reviewing the patients\u0026rsquo; medical records. All patients underwent routine preoperative laboratory studies, including CA-125, CA-19-9, carcinoembryonic antigen (CEA) and a complete blood count test was performed prior to surgery. Patient-related factors assessed included endometriosis-related symptoms, age, body mass index (BMI), reproductive history (mature delivery, premature delivery, abortion), menstrual history (menstrual cycle, duration of menstrual flow), surgical features (history of laparotomy, laparoscopy or hysteroscopy) and histopathology diagnosis following surgery were retrieved for each patient.\u003c/p\u003e \u003cp\u003eMost patients in the study had preoperative ultrasonographic evaluations. The reasons why we do not bring in the ultrasonographic diagnosis of cysts in the study were that sonographic evaluations were performed by different sonographers, transducers and types of ultrasounds.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003ePatients characteristics were compared by using variance analysis (t-test, for continuous variables), chi-square test (for dichotomous variables) or Kruskal-Wallis test (for continuous variables in skewed distribution) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Logistic regression was used to perform multivariate analysis, and forward method was used to select variables[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. 10-fold cross validation was used to predict the accuracy of the internal validation model. We randomly divided the data set into 10 copies, with 9 of them as a training set, used to establish the forecast model, the remaining 1 data set as a validation set, as a validation set. Training set to build the model, with the validation set to predict, this process was continued for 10 times and ultimately obtained a complete set of predictions. The predicted value was used to establish a cross-validated ROC curve. At the same time, 10 models were established, and 10 normalized mean square errors (NMSE) were obtained for the verification set and the average NMSE of the model was obtained to compare the reliability of the model. The smaller the NMSE, the more reliable the model is. Delong method was used to compare the significant difference between ROC curves, in which P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated the difference was statistically significant. The Hosmer-Lemeshow goodness-of-fit test was used to test the predictive ability (calibration) of the model[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The model was evaluated with different indicators, including sensitivity, specificity, area under curve (AUC), and Youden Index, where the signature\u0026thinsp;=\u0026thinsp;sensitivity \u0026minus;\u0026thinsp;(1-specificity). SPSS 13.0 for statistical analysis, R 3.2.3 software pROC package to do the Receiver Operating Curve (ROC) and ROC curve comparison. Bilateral test P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 showed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatients Characteristics:\u003c/h2\u003e \u003cp\u003eA total of 2242 women were enrolled in the study and the age ranges from 18 to 46 years, with a mean age of 31.37 years. 978 patients had endometriosis while the rest were 860 patients with mature teratoma,145 patients with serous cystadenomas, 183 patients with mucinous cystadenomas, and 76 patients with other benign conditions. The characteristics included baseline demographic, clinical, surgical, and laboratory features of patients with and without endometriosis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Characteristics Between Endometriosis with Benign Ovarian Cysts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-endometriosis (n\u0026thinsp;=\u0026thinsp;1264)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;978)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-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)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-3.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMature delivery(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-4.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremature delivery(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbortion (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual cycle(days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;6.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of menstrual flow (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-6.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular menstruation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;35.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysmenorrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;75.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;1.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of caesarean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;2.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of laparotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375 (29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;3.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of laparoscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hysteroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;21.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA(U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (0-95.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0-4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-7.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA-125(U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.54 (3.10-469.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.35 (4.80-660.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-31.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA-19-9(U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.13 (0-1147.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.84 (0-1128.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-6.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count(X10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.96 (1.16\u0026ndash;19.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86 (1.25\u0026ndash;14.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count(X10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83 (0.31\u0026ndash;4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76 (0.25\u0026ndash;4.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-3.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\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\u003e2.17 (0.56\u0026ndash;31.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22 (0.65\u0026ndash;21.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-1.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonocyte count (X10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.09\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42 (0.11\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-1.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (X103/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211.00 (72.00-516.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213.00 (61.00-418.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.70 (6.90\u0026ndash;14.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.70 (6.40\u0026ndash;14.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.00 (62.00-156.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.00 (65.00-149.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-5.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.45 (56.80-103.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.30 (60.20\u0026ndash;99.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.80 (18.00-35.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.60 (18.20\u0026ndash;34.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-3.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335.00 (280.00-368.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.00 (288.00-363.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-4.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: BMI, body mass index; MPV, mean platelet volume; NLR, neutrophil-to-lymphocyte ratio; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered as statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eClinicopathological Features With Endometriosis:\u003c/h2\u003e\n\u003cp\u003eAfter analysis of endometriosis with clinicopathological features, we found that there was no statistical significance with premature delivery (P\u0026thinsp;=\u0026thinsp;0.192), abortion (P\u0026thinsp;=\u0026thinsp;0.824), hyperlipidemia (P\u0026thinsp;=\u0026thinsp;0.256), history of caesarean (P\u0026thinsp;=\u0026thinsp;0.143), history of laparotomy (P\u0026thinsp;=\u0026thinsp;0.143), history of laparoscopy (P\u0026thinsp;=\u0026thinsp;0.143), Neutrophil count (P\u0026thinsp;=\u0026thinsp;0.806), NLR (P\u0026thinsp;=\u0026thinsp;0.078), monocyte count (P\u0026thinsp;=\u0026thinsp;0.079), Platelet count (P\u0026thinsp;=\u0026thinsp;0.443), MPV (P\u0026thinsp;=\u0026thinsp;0.417) and MCV (P\u0026thinsp;=\u0026thinsp;0.252). Further analysis showed that age, BMI, mature delivery, menstrual cycles, duration of menstrual flow, irregular menstruation, dysmenorrhea, history of hysteroscopy, CEA, CA 125, CA-19-9, hemoglobin, MCHC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lymphocyte count (P\u0026thinsp;=\u0026thinsp;0.001) and MCH (P\u0026thinsp;=\u0026thinsp;0.001) were statistically associated with endometriosis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003eUnivariate And Multivariate Analysis:\u003c/h2\u003e\n\u003cp\u003eWe selected 16 variables to establish the multivariate logistic regression analysis. As a result, 11 variables were retained in the final logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), it revealed that Log(CA-125), dysmenorrhea, history of hysteroscopy, age and mature delivery remained as significant variables associated with endometriosis. The Hosmer\u0026ndash;Lemeshow test results revealed an adequate goodness-of-fit for the regression model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The association between characteristics and diagnosis of endometriosis was explored and shown in (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), all the models (model 1\u0026thinsp;~\u0026thinsp;model 5) were significantly associated with endometriosis. A ROC curve for the model with CA-125 alone was constructed (Fig.\u0026nbsp;1), the area under the curve was 0.888 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for the model, with a sensitivity of 0.816% and specificity of 0.835%, These results indicate a moderate predictive performance of the model,after adjustment for other multiple covariates, the result presented a little rise of accuracy in diagnosis of endometriosis. The modle5 of included other values (combined clinical characteristics and haematological indexes) in the CA-125 based probabilistic model showed an AUC of 0.916 (\u003cem\u003eP\u003c/em\u003e ༜001), a sensitivity of 0.849 and specificity of 0.864 (Fig.\u0026nbsp;2), The Hosmer\u0026ndash; Lemeshow test (p\u0026thinsp;=\u0026thinsp;0.060) in the combined model5 indicate a good fitness of the model characteristic (dysmenorrhea, Irregular menstruation,), laboratory characteristics (Platelet count, monocyte count, CA-19-9, CA-125, CEA).\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\u003eMultivariate logistic regression model of morbidity risk in endometriosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eS.E.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWals\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e95% CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP- Value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.126\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\u003e87.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.105\u0026ndash;1.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emature delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.422\u0026ndash;0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular menstruation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.512\u0026ndash;4.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysmenorrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.337\u0026ndash;2.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.183\u0026ndash;1.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hysteroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.921\u0026ndash;29.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.339\u0026ndash;0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA-19-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.118\u0026ndash;0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.995\u0026ndash;1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog(CA-125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e528.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1162.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e637\u0026ndash;2122.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-13.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e287.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression models fitting results of the association between non-endometriosis and endometriosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eH-L\u003c/em\u003e test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC\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 \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYouden index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNMSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 5\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e Only CA125 in the model, \u003csup\u003eb\u003c/sup\u003e variables in model 1 plus age, \u003csup\u003ec\u003c/sup\u003e variables in model 2 plus reproductive history, \u003csup\u003ed\u003c/sup\u003e variables in model 3 plus mature delivery, history of hysteroscopy, clinical characteristic, \u003csup\u003ee\u003c/sup\u003e variables in model 4, except CA125 was replaced by log(CA125)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEndometriosis can only be diagnosed by invasive procedures such as laparoscopic or laparotomy exploration. We constructed a non-invasive predictive model based on medical history and hematological indexes (blood routine, serum tumor markers examination) that can diagnose endometriosis in ovarian cyst patients. We found association between the CA-125, CA-19-9, age, partus matures, menstrual episodes, history of hysteroscopy, dysmenorrhea and blood routine test with endometriosis, but no single characteristic predicted endometriosis with a high accuracy. Our study supported the retrograde menstruation theory because the history of hysteroscopy is shown to be associated with an increase in risk of developing endometriosis.\u003c/p\u003e \u003cp\u003eOur study confirmed the belief that an increased frequency of and duration of menstruations is associated with endometriosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Dysmenorrhea was the main symptom of endometriosis infertile women (46.92%) with endometriosis and the mechanism of dysmenorrhea in endometriosis lie in increased production of prostaglandins (PGs)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, BMI showed a negative correlation with the presence of endometriosis, as was reported previously[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Obesity is often associated with long menstrual cycles, a factor that reduce the risk of endometriosis. It is considered that the reduction of the frequency of menstrual episodes counterbalances the relative hyperestrogenism of women[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEndometriosis is rare before the menarche and tends to decrease after the menopause. Studies conducted in women under age of 45 years suggested that the frequency of endometriosis increases with age until menopause[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While Fuldeore, M. J, et al [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] reported that the average age of women with endometriosis in their study was 37.8 years compared to 33.8 years women without endometriosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), it is possible that incidence of endometriosis increases as women age increases which can be because of the hormonal changes that occur during peri-menopause[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eScreening for the diagnosis of patients with clinical suspicion of endometriosis is based on serum CA-125 which have been confirmed in many studies. Shen, A et al [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported that endometriosis is significantly associated with elevated serum CA-125 concentrations, confirmed CA-125 as an auxiliary biological marker in endometriosis diagnosis. Some studies[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] did not agree with this finding and showed that the diagnosis of endometriosis on CA-125 alone is not accurate, mainly in relation to their sensitivity, Hirsch, M, et al[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] reported that CA-125 with a cut-off of \u0026ge;\u0026thinsp;30 u/ml has a sensitivity of 0.57, which did not meet the criteria for a triage test, and international guidelines do not recommend CA-125 testing in women with suspected endometriosis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, in the study we find model 1 which consisted of CA-125 alone predicted endometriosis with high sensitivity (81.6%) and predicted the absence of endometriosis with a specificity of 83.5%. Nevertheless the timing of blood collection for CA-125 is uncontrolled because it\u0026rsquo;s a retrospective design, the relationship with the menstrual cycle is known to affect this test[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study shows an inverse association between the number of mature delivery and endometriosis, but no association between the number of abortions and endometriosis has been found. This has also been observed in many studies of endometriosis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Parazzini, F, et al [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]reported that the risk of endometriosis decreased with increasing number of births, compared with nulliparous women, the OR of endometriosis at stage 1 was 0.1 (95% CI 0.1, 0.2) in women reporting two or more births was respectively 0.1 (95% CI 0.1, 0.3), 0.2 (95% CI 0.1, 0.4).\u003c/p\u003e \u003cp\u003eIt has been reported that [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] reproductive history may influence hormonal milieu, Estradiol levels is higher among nulliparous women than among parous women, whereas androgen levels have an opposite effect, and reproductive history may influence the volume of endometrial cells released into the peritoneal cavity. The other studies revealed that CA-19-9 can be used to discriminate between patients with or without endometriosis, and it is correlation to severity of the disease, their results showed that CA-19-9 was significantly associated with advanced stage (stage III and IV) endometriosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our results is in concordance with a former study that the mean levels of CA-19-9 are significantly elevated compared with the control group.\u003c/p\u003e \u003cp\u003eEndometriosis is associated with increased inflammatory activity which is an important stimulant for platelets[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], suggest platelet indices is an important and effortless hematological parameter that can be useful in evaluation of endometriosis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Evsen, M. S et al reported that platelets count in patients with peritoneal endometriosis were found to be higher from the control group (p\u0026thinsp;=\u0026thinsp;0.038)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], particularly more apparent in advanced stage peritoneal endometriosis. Monocytes also was also implicated as prognostic factor of inflammatory response but there is no evidence supporting that monocyte count is associated with endometriosis, our study shows that it is a protective factor for endometriosis, further study should be done to confirm this finding. This model provides guidance about confirmation of endometriosis. CA-125 can be useful in directing the diagnosis of the disease, and clinical history, tumor marker and routine blood tests increase the diagnosis of endometriosis more accurately. For instance, a peri-menopause woman with multiple reproductive history and irregular menstruation, has a higher chance of containing endometriosis, and if CA-125 is quite high, ovarian cysts would be appropriate to confirm the presence of the disease.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we found that CA-125, clinical history, tumor marker and routine blood term testing are predictors of endometriosis. Our model can contribute in diagnosis as a predictor for endometriosis in patients with ovarian cysts.\u003c/p\u003e "},{"header":"List Of Abbreviations","content":" \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull name\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecarcinoembryonic antigen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enormalized mean square errors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003earea under curve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereceiver operating curve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained from Second Affiliated Hospital of Wenzhou Medical University Ethics Committee, and informed consent was obtained from individual participants prior to surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was used in this conduct of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunmiao Xiang contributed to the conception of the study.\u003c/p\u003e\n\u003cp\u003ePing Duan contributed significantly to analysis and manuscript preparation.\u003c/p\u003e\n\u003cp\u003eWei Shen performed the data analyses.\u003c/p\u003e\n\u003cp\u003eZongwen Liang and Qiong Zhang helped perform the analysis with constructive discussions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to express my gratitude to all those who helped me during the writing of this paper. My deepest gratitude goes first and foremost to Professor Ping Duan, my supervisor, for her constant encouragement and guidance. And the author gratefully acknowledge the support of the Second Affiliated Hospital of Wenzhou Medical.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGiudice LC, Kao LC. Endometriosis. The Lancet. 2004;364(9447):1789\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuldeore MJ, Soliman AM. Prevalence and Symptomatic Burden of Diagnosed Endometriosis in the United States: National Estimates from a Cross-Sectional Survey of 59,411 Women. Gynecol Obstet Invest. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalme J, Hammond MG, Hulka JF, Raj SG, Talbert LM. Retrograde menstruation in healthy women and in patients with endometriosis. Obstet Gynecol. 1984;64(2):151\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallard KD, Seaman HE, de Vries CS, Wright JT. Can symptomatology help in the diagnosis of endometriosis? Findings from a national case-control study - Part 1. BJOG. 2008;115(11):1382\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLattarulo S, Pezzolla A, Fabiano G, Palasciano N. Intestinal endometriosis: role of laparoscopy in diagnosis and treatment. Int Surg. 2009;94(4):310\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEskenazi B, Warner M, Bonsignore L, Olive D, Samuels S, Vercellini P. Validation study of nonsurgical diagnosis of endometriosis. Fertil Steril. 2001;76(5):929\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigano P, Parazzini F, Somigliana E, Vercellini P. Endometriosis: epidemiology and aetiological factors. Best Practice Research in Clinical Obstetrics Gynaecology. 2004;18(2):177\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta D, Hull ML, Fraser I, Miller L, Bossuyt PMM, Johnson N, et al. Endometrial biomarkers for the non-invasive diagnosis of endometriosis. Cochrane Database Syst Rev. 2016;4:Cd012165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNisenblat V, Bossuyt PMM, Shaikh R, Farquhar C, Jordan V, Scheffers CS, et al. Blood biomarkers for the non-invasive diagnosis of endometriosis. Cochrane Database Syst Rev, 2016(5): p. Cd012179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurdoglu Z, Gursoy R, Kurdoglu M, Erdem M, Erdem O, Erdem A. Comparison of the clinical value of CA-19-9 versus CA 125 for the diagnosis of endometriosis. Fertil Steril. 2009;92(5):1761\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z. Univariate description and bivariate statistical inference: the first step delving into data. Ann Transl Med. 2016;4(5):91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z. Variable selection with stepwise and best subset approaches. Ann Transl Med. 2016;4(7):136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z. Residuals and regression diagnostics: focusing on logistic regression. Ann Transl Med. 2016;4(10):195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurghaus S, Klingsiek P, Fasching PA, Engel A, Haberle L, Strissel PL, et al. Risk Factors for Endometriosis in a German Case-Control Study. Geburtshilfe Frauenheilkd. 2011;71(12):1073\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCramer DW, Wilson E, Stillman RJ, Berger MJ, Belisle S, Schiff I, et al. The relation of endometriosis to menstrual characteristics, smoking, and exercise. JAMA. 1986;255(14):1904\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoike H, Ikenoue T, Mori N. [Studies on prostaglandin production relating to the mechanism of dysmenorrhea in endometriosis]. Nihon Naibunpi Gakkai Zasshi. 1994;70(1):43\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLafay Pillet MC, Schneider A, Borghese B, Santulli P, Souza C, Streuli I, et al. Deep infiltrating endometriosis is associated with markedly lower body mass index: a 476 case-control study. Hum Reprod. 2012;27(1):265\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalhaz-Jorge C, Mol BW, Nunes J, Costa AP. Clinical predictive factors for endometriosis in a Portuguese infertile population. Hum Reprod. 2004;19(9):2126\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenberg VH, Weil C, Chodick G, Shalev V. Epidemiology of endometriosis: a large population-based database study from a healthcare provider with 2 million members. BJOG. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSA M, DW. C. The epidemiology of endometriosis. Obstet Gynecol Clin North Am. 2003;30(1):1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen A, Xu S, Ma Y, Guo H, Li C, Yang C, et al. Diagnostic value of serum CA-125, CA19-9 and CA15-3 in endometriosis: A meta-analysis. J Int Med Res. 2015;43(5):599\u0026ndash;609.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosa ESAC, Rosa ESJC, Ferriani RA. Serum CA-125 in the diagnosis of endometriosis. Int J Gynaecol Obstet. 2007;96(3):206\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirsch M, Duffy JMN, Deguara CS, Davis CJ, Khan KS. Diagnostic accuracy of Cancer Antigen 125 (CA-125) for endometriosis in symptomatic women: A multi-center study. 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Obstet Gynecol. 2004;104(5 Pt 1):965\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBullon P, Navarro JM. Inflammasome as a key pathogenic mechanism in endometriosis. Curr Drug Targets. 2017;18(9):997\u0026ndash;1002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvcioglu SN, Altinkaya SO, Kucuk M, Demircan-Sezer S, Yuksel H. Can platelet indices be new biomarkers for severe endometriosis? ISRN Obstet Gynecol. 2014;2014:713542.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvsen MS, Soydinc HE, Sak ME, Ozler A, Turgut A, Celik Y, et al. Increased platelet count in severe peritoneal endometriosis. Clin Exp Obstet Gynecol. 2014;41(4):423\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\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":"Endometriosis, ovarian cyst, CA-125, CA-19-9, logistic regression modeling","lastPublishedDoi":"10.21203/rs.3.rs-762131/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-762131/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTo develop a model that uses hematological indexes and clinical characteristics to help estimate the probability of endometriosis in patients with ovarian cysts.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA retrospective study was conducted on 2242 patients who underwent surgery for benign ovarian cysts from January 2008 to\u0026nbsp;November 2016. Variables included in the model were serum\u0026nbsp;tumor markers, blood routine test, age, BMI, reproductive\u0026nbsp;history, history of hysteroscopy, menstrual episodes. Logistic regression was used to construct a predictive model for endometriosis, Receiver Operating Characteristic curves and the areas under the curve was used to verify the model’s validities. Ten-fold cross-validation was primarily used as an internal validation to evaluate the prediction accuracies of the model. Normalized mean square errors (NMSE) was obtained to compare the reliability of different models.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e978 (43.6%) patients with endometriosis were included in the strudy. Univariate analysis showed that age, BMI, delivery, dysmenorrhea, menstrual cycle, duration of menstrual flow, history of hysteroscopy, CA-125 and CA-19-9 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) are associated with endometriosis. The area under the receiver operating characteristic curve for the model with CA-125 alone was 0.888, with a sensitivity of 81.6% and specificity of 83.5%. After adjustment for other multiple covariates, including age, mature delivery, irregular menstruation, dysmenorrhea, menstrual period, history of hysteroscopy, CEA, CA-19-9, monocyte count, platelet count, the model obtained had an AUC of 0.916, with a sensitivity of 0.849 and specificity of 0.864.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic prediction model can be used as a framework for potential improvement in diagnosis of endometriosis in patient with ovarian cyst.\u003c/p\u003e","manuscriptTitle":"A Novel Model To Predict Endometriosis in Patients with Ovarian Cysts: A Retrospective Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-12 13:28:55","doi":"10.21203/rs.3.rs-762131/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":"7c2a63a8-c9f6-4537-b07f-d8f35c7781e7","owner":[],"postedDate":"August 12th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6397511,"name":"Sexual \u0026 Reproductive Medicine"},{"id":6397512,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2021-08-19T11:26:14+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-12 13:28:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-762131","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-762131","identity":"rs-762131","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}