Quantitative Analysis of the MRI Features in the Differentiation of Benign, Borderline, and Malignant Epithelial Ovarian Tumors

Quantitative Analysis of the MRI Features in the Differentiation of Benign, Borderline, and Malignant Epithelial Ovarian Tumors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative Analysis of the MRI Features in the Differentiation of Benign, Borderline, and Malignant Epithelial Ovarian Tumors Fuxia Xiao, Lin Zhang, Sihua Yang, Kun Peng, Ting Hua, Guangyu Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-350729/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective : This study aims to investigate the value of the quantitative indicators of MRI in the differential diagnoses of benign, borderline, and malignant epithelial ovarian tumors (EOTs). Materials and Methods : The study population comprised 477 women with 513 masses who underwent MRI and operation, including benign EOTs (BeEOTs), borderline EOTs (BEOTs), and malignant EOTs (MEOTs). The clinical information and MRI findings of the three groups were compared. Then, multivariate logistic regression analysis was performed to find the independent diagnostic factors. The receiver operating characteristic (ROC) curves were also used to evaluate the diagnostic performance of the quantitative indicators of MRI and clinical information in differentiating BeEOTs from BEOTs or differentiating BEOTs from MEOTs. Results : The MEOTs likely involved postmenopausal women and showed higher CA-125, HE4 levels, ROMA indices, peritoneal carcinomatosis and bilateral involvement than BeEOTs and BEOTs. Compared with BEOTs, BeEOTs and MEOTs appeared to be more frequently oligocystic ( P < 0.001). BeEOTs were more likely to show mild enhancement ( P < 0.001) and less ascites ( P = 0.003) than BEOTs and MEOTs. In the quantitative indicators of MRI, BeEOTs usually showed thin-walled cysts and no solid component. BEOTs displayed irregular thickened wall and less solid portion. MEOTs were more frequently characterized as solid or predominantly solid mass ( P < 0.001) than BeEOTs and BEOTs. The multivariate logistic regression analysis showed that volume of the solid portion ( P = 0.006) , maximum diameter of the solid portion( P = 0.038), enhancement degrees ( P < 0.001), and peritoneal carcinomatosis ( P = 0.011) were significant indicators for the differential diagnosis of the three groups. The area under the curves (AUCs) of above indicators and combination of four image features except peritoneal carcinomatosis for the differential diagnosis of BeEOTs and BEOTs, BEOTs and MEOTs ranged from 0.74 to 0.85, 0.58 to 0.79, respectively. Conclusion : In this study, the characteristics of MRI can provide objective quantitative indicators for the accurate imaging diagnosis of three categories of EOTs and are helpful for clinical decision-making. Among these MRI characteristics, the volume, diameter, and enhancement degrees of the solid portion showed good diagnostic performance. Sexual & Reproductive Medicine Cancer Biology Ovarian Neoplasms Magnetic resonance imaging Differential diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Epithelial ovarian tumor (EOT) is the most common type in the classification of ovarian tumors and are categorized as benign (BeEOTs), borderline (BEOTs), and malignant (MEOTs) on the basis of histological results. Ovarian tumors remain the first indication for gynecologic surgery [ 1 – 2 ]. Laparoscopic tumor exfoliation or unilateral ovariectomy can be performed if the mass is a small BeEOT [ 3 – 6 ]. Young patients with BEOTs can undergo conservative surgery to preserve fertility or maintain ovarian function [ 7 – 10 ], whereas patients with MEOTs require the radical resection of tumors, followed by adjuvant chemotherapy [ 10 – 14 ]. Thus, the accurate diagnosis of the preoperative subtype of EOTs is important for the patient’s therapeutic schedule and prognosis. This study aims to analyze the quantitative indicators of magnetic resonance (MR) image for the accurate diagnosis of EOTs and explore the weight of those features in the differential diagnoses of the three types of EOTs through multiple regression analysis. Material And Methods Patients All patients with EOTs who underwent preoperative MRI from our picture archiving and communication system (PACS) database and had pathological results between January 1, 2009 and August 31, 2018 were retrospectively recruited. The subjects consisted of 477 patients with 513 EOTs. A total of 441 women had one mass, and 36 women had two masses. The population characteristics and biochemical examinations are shown in Table 2 . The recruit tumors were categorized into the BeEOTs, BEOTs, and MEOTs groups on the basis of the pathological results. This retrospective study was approved by the institutional review board with the waiver of the informed consent. MRI technique The MR images were acquired using the 3.0-T MR imaging unit (Magnetom Verio, Siemens Medical Solutions, Germany) by employing a pelvic phased-array coil. The following imaging sequences were performed: transverse nonfat-suppressed T2-weighted turbo spin-echo sequences (repetition time [TR], 4050 ms; echo time [TE], 84 ms; section thickness, 4 mm; field of view (FOV), 325 mm; matrix, 384 × 256; and number of excitations [NEX] 2), transverse nonfat-suppressed T1-weighted gradient-echo sequences (TR, 550 ms; TE, 13 ms; section thickness, 4 mm; FOV, 325 mm; matrix, 384 × 256; and NEX, 2), sagittal fat-suppressed T2-weighted turbo spin-echo sequences, and coronal nonfat-suppressed T2-weighted turbo spin-echo sequences. Then, dynamic contrast-enhanced MRI (DCE-MRI) with 3D fat-suppressed T1-weighted interpolated spoiled gradient-echo sequence with volumetric interpolated breath-hold examination was performed in the transverse, sagittal, and coronal planes at scanning delay times of 40 and 120 s after the bolus injection (2.5 mL/s) of gadopentetate dimeglumine (0.5 mol/L, Beijing Beilu Pharmaceutical Company) at a dose of 0.1 mmol/kg, followed by 50 mL saline flush through the antecubital vein. MR images analysis Two radiologists who were blinded to the pathological results independently reviewed the MR images and collected the clinical information of the patients. The characteristics of MRI include volume of tumor, maximum diameter of tumor, septum thickness, volume of solid portion, volume ratio of solid portion, maximum diameter of solid portion, maximum diameter ratio of solid portion, number of cysts, peritoneal carcinomatosis, ascites, bilateral involvemen. The criteria of MRI were elaborated on the basis of several previously published terms (Table 1 ). Table 1 Definition of MRI findings Term Reference Definition Measurement standard Septum thickness Timmerman et al[ 15 ]. Thickness of septum or septa within a cystic tissue If the septum is irregular, select the thickest focal area. Volume of tumor GAO Mei-chun[ 16 ] - The volume of tumors was estimated in PACS by measuring the area of the tumor on contiguous 3.0 mm thick transverse slices throughout the whole length of tumor by using manually drawn boundaries. The area was generated automatically, and the volume of tumors were calculated by multiplying the slice thickness with the sum of the tumor cross-sectional area (Cavalieri’s principle) Volume of solid portion Timmerman et al[ 15 ]. As defined by the IOTA group, at MR imaging, solid tissue enhances after gadolinium chelate injection. Therefore, the solid tissue includes vegetation. The method of measurement was the same as that of the “volume of tumor”. Volume ratio of solid portion The proportion of solid components in the total tumorous volume =Volume of solid portion/Volume of tumor Maximum diameter of tumor The diameter of the largest level of the tumor - Maximum diameter of solid portion The diameter of the largest level of the tumorous solid portion - Maximum diameter ratio of solid portion The ratio of the maximum diameter of solid portion and the maximum diameter of tumor =Maximum diameter of solid portion/Maximum diameter of tumor Statistical Analysis Statistical analysis was performed using the SPSS 20.0 (SPSS, Inc., Chicago, IL, USA). Continuous variables, such as patient’s age and serum carbohydrate antigen 125 (CA-125) level, were expressed as mean ± standard deviation. The kappa and intraclass correlation (ICCs) coefficients were calculated to assess the interobserver agreement between the two readers for imaging parameter analysis. A kappa value of 0.00–0.20, 0.21–0.40, 0.41–0.60, 0.61–0.80, and 0.81–1.00 indicated slight, fair, moderate, substantial, and almost perfect agreement, respectively[ 17 ]. An ICC value of 0.00–0.10 indicated virtually no agreement, and ICC values of 0.11–0.40, 0.41–0.60, 0.61–0.80, and 0.81–1.00 indicated slight, fair, moderate, and substantial agreement, respectively[ 18 ]. In order to identify significant differences in MR imaging parameters, population characteristics and biochemical examinations, the Kruskal-Wallis test was used for continuous variables and categorical data among three groups. Multivariate logistic regression analysis was performed using all qualitative and quantitative variables to find the independent diagnostic factors. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of MR characteristics and clinical information in differentiating BeEOTs, BEOTs, and MEOTs. ROC analysis was performed using the Medcalc version 15.6 (MedCalc Software, Mariakerke, Belgium). A P value < 0.05 was considered statistically significant. Results Clinical evaluation The population characteristics and biochemical examinations of the blood samples of 477 patients with 513 ovarian masses are demonstrated in Table 2 . Their mean age was 52.36 ± 12.71 (range 18–86) years. A total of 208 (43.61%) women were premenopausal, and 269 (56.39%) were postmenopausal. Thirty-six (7.55%) patients had bilateral tumors, and 441 (92.45%) patients had unilateral tumors. The significant differences were obtained for all indicators, including age, postmenopause, CA-125 level, human epididymis protein 4 (HE4), and premenopausal and postmenopausal risk of ovarian malignancy algorithm (ROMA) indices. Table 2 Population clinical characteristics and biochemical examinations of blood BeEOTs(n = 330, n * =347, n ※ =305) BEOTs(n = 49, n * =50, n ※ =48) MEOTs(n = 98, n * =116, n ※ =109) P value Age 48.20 ± 13.04 47.61 ± 17.14 56.44 ± 7.79 0.001 Postmenopausal 0.002 No 148(44.85) 26(53.06) 25(25.51) Yes 182(55.15) 23(46.94) 73(74.49) CA-125 19.92 ± 29.00 89.82 ± 191.24 523.92 ± 835.60 <0.001 HE 4 51.92 ± 16.91 100.12 ± 124.28 260.23 ± 239.23 <0.001 Premenopausal ROMA index 9.23 ± 6.14 9.36 ± 5.77 47.81 ± 35.52 <0.001 Postmenopausal ROMA index 11.08 ± 3.68 20.06 ± 15.98 57.95 ± 30.22 0.001 The case number of BeEOTs (n) is 330 ( unilateral 313, bilateral 17) with 347 tumors (n * ). The number of tumors with contrast enhanced MR imaging(n ※ ) is 305. The case number of BEOTs (n) is 49 ( unilateral 48, bilateral 1) with 50 tumors (n * ). The number of tumors with contrast enhanced MR imaging (n ※ ) is 48. The case number of MEOTs (n) is 98 ( unilateral 80, bilateral 18) with 116 tumors (n * ). The number of tumors with contrast enhanced MR imaging (n ※ ) is 109. The number in parenthesis is the percentage. Interobserver Agreement For all MR imaging variables, the interobserver agreement was good (ICC = 0.899 − 0.999, kappa = 0.932 − 0.978; Table 3 ). Table 3 Interobserver agreement of MR imaging variables MR Imaging Variables Κ value ICC(95%CI) Volume of tumor - 0.988(0.985–0.991) Volume of solid portion - 0.899(0.870–0.922) Volume ratio of solid portion - 0.982(0.976–0.986) Maximum diameter of tumor - 0.988(0.985–0.991) Maximum diameter of solid portion - 0.995(0.994–0.997) Maximum diameter ratio of solid portion - 0.999(0.998–0.999) Enhancement degrees 0.965 - Ascites 0.978 - Peritoneal carcinomatosis 0.932 - MR image analysis Table 4 shows the characteristics of the MR imaging findings in EOTs among benign, borderline, and malignant lesions by using univariate analysis. Compared with BEOTs, BeEOTs and MEOTs had less cysts (23/50, 46 % vs 311/347, 89.63 % and 88/116, 75.86 %, P < 0.001). Most BeEOTs had mild enhancement (290/305, 95.08 % vs 16/48, 33.33 % and 9/109, 8.26 %, P < 0.001) and less frequent ascites (75/330, 22.72 % vs 27/49, 55.10 % and 65/98, 66.33%, P = 0.003) than BEOTs and MEOTs. Peritoneal carcinomatosis was found in 24.49 % (24/98) of MEOTs, 0% of BeEOTs, and 2.04% (1/49) of BEOTs ( P < 0.001), and bilateral involvement were more frequent in MEOTs (18.37%, 18/98) than in BeEOTs (6.06 %, 20/330) and BEOTs (2.04%, 1/49) ( P = 0.002, Figs. 1 – 3 ). In quantitative MR imaging descriptors, BeEOTs usually showed thin-walled cysts and no solid component, but BEOTs often displayed irregular thickened walls and small amount of solid portion. MEOTs were more frequently characterized as completely solid or predominantly solid mass ( P < 0.001, Figs. 1 – 3 ). No statistical difference was found among the three groups in terms of volume of tumor and maximum diameter of tumor( P = 0.058, P = 0.055, respectively). Table 4 The difference of MRI parameters among three groups of EOTs BeEOTs(n = 330, n * =347, n ※ =305) BEOTs(n = 49, n * =50, n ※ =48) MEOTs(n = 98, n * =116, n ※ =109) P value Septum thickness 0.24 ± 0.11 0.53 ± 0.41 0.77 ± 0.34 <0.001 Volume of tumor 483.30 ± 883.11 1106.15 ± 2000.28 412.88 ± 674.36 0.058 Volume of solid portion 0.00 ± 0.00 57.23 ± 163.74 79.63 ± 120.08 <0.001 Volume ratio of solid portion 0.00 ± 0.00 9.58 ± 19.98 43.36 ± 37.49 <0.001 Maximum diameter of tumor 9.02 ± 6.19 12.44 ± 7.63 9.47 ± 4.50 0.055 Maximum diameter of solid portion 0.00 ± 0.00 2.52 ± 3.60 5.25 ± 3.03 <0.001 Maximum diameter ratio of solid portion 0.00 ± 0.00 22.84 ± 30.11 62.56 ± 33.19 <0.001 Bilateral involvement 0.002 No 310(93.94) 48(97.96) 80(81.63) Yes 20(6.06) 1(2.04) 18(18.37) Number of cysts <0.001 10 21(6.05) 24(48.00) 15(12.93) Enhancement degrees <0.001 Mild 290(95.08) 16(33.33) 9(8.26) Moderate 0(0) 4(8.33) 29(26.60) Prominent 15(4.92) 28(58.34) 71(65.14) Ascites 0.003 No 255(77.27) 22(44.90) 33(33.67) Yes 75(22.72) 27(55.10) 65(66.33) Peritoneal carcinomatosis <0.001 No 330(100) 48(97.96) 74(75.51) Yes 0(0) 1(2.04) 24(24.49) Multivariate logistic regression analysis was performed to obtain independent differential diagnostic factors. Results are shown in Table 5 . The outcome revealed that volume of solid portion( P = 0.006), maximum diameter of solid portion( P = 0.038), enhancement degrees( P < 0.001), and peritoneal carcinomatosis( P = 0.011) were significant indicators for differentiate diagnosis of the three groups. Table 5 Multivariate Logistic Regression of MR imaging parameters in EOTs Covariate Regression coefficient Standard error Wald P value OR Age 0.001 0.022 0.002 0.964 1.00 Postmenopausal 0.851 0.597 2.029 0.154 2.34 Volume of tumor 0.000 0.000 0.154 0.695 1.00 Volume of solid portion -0.008 0.003 7.520 0.006 * 0.99 Volume ratio of solid portion 0.026 0.021 1.515 0.218 1.03 Maximum diameter of tumor 0.008 0.056 0.022 0.883 1.01 Maximum diameter of solid portion 0.453 0.218 4.328 0.038 * 1.57 Maximum diameter ratio of solid portion 0.004 0.024 0.022 0.883 1.00 Bilateral involvement 1.076 0.881 1.492 0.222 2.93 Number of cysts -0.236 0.244 0.930 0.335 0.79 Enhancement degrees 1.289 0.256 25.275 0.000 * 3.63 Ascites 0.235 0.409 0.329 0.566 1.26 Peritoneal carcinomatosis 3.039 1.191 6.507 0.011 * 20.88 * indicate a significant difference among three groups. Then, the diagnostic performance of MR imaging parameters, including volume of solid portion, maximum diameter of solid portion, enhancement degrees, peritoneal carcinomatosis, and their combination were assessed and compared using ROC analyses to differentiate two groups. The area under the curve(AUC), sensitivity, specificity, positive predictive value(PPV) and negative predictive value(NPV) of this multivariate logistic regression model are shown in Table 6 and Fig. 4 . In comparing BeEOTs and BEOTs, the image features of volume of solid portion, maximum diameter of solid portion, enhancement degrees, and the combination of four image features revealed moderate diagnostic value (0.74, 0.74, 0.8, 0.85, respectively), whereas peritoneal carcinomatosis showed low diagnostic value (0.51). Moreover, the above indicators except enhancement degrees (0.58) and peritoneal carcinomatosis(0.61) demonstrated moderate diagnostic value in BEOTs and MEOTs (0.78, 0.76,0.79, respectively). Table 6 Receiver operating characteristic analysis of MR imaging parameters sensitivity specificity AUC PPV(%) NPV(%) Volume of solid portion BeEOTs vs BEOTs 46.94 100.00 0.74 100.00 61.19 BEOTs vs MEOTs 89.80 63.27 0.78 84.43 70.45 Maximum diameter of solid portion BeEOTs vs BEOTs 46.94 100.00 0.74 100.00 61.19 BEOTs vs MEOTs 86.73 67.35 0.76 85.47 67.35 Enhancement degrees BeEOTs vs BEOTs 65.96 94.44 0.80 94.12 68.63 BEOTs vs MEOTs 90.11 34.04 0.58 75.76 64.00 Peritoneal carcinomatosis BeEOTs vs BEOTs 2.04 100.00 0.51 100.00 45.45 BEOTs vs MEOTs 24.49 97.96 0.61 96.00 39.34 Combination BeEOTs vs BEOTs 74.47 94.44 0.85 94.59 73.91 BEOTs vs MEOTs 86.81 68.09 0.79 84.04 46.38 Discussion Adnexal masses, in general, are first found and evaluated using ultrasonography[ 19 ]. Nevertheless, in a prospective randomized trial in 2010, a consensus conference of the Society of Radiologists in Ultrasound proposed that establishing structured standards for adnexal cysts is needed[ 20 ]. To date, numerous scoring systems for preoperative mass discrimination have been developed[ 21 ]. Fernando Amor et al.[ 22 , 23 ] proposed the Gynecologic Imaging Reporting and Data System (GI-RADS) to guide every imaging modality in describing and categorizing ovarian lesions in ultrasonography, but they did not specify the basis of classification and the imaging evidence for each category crucial to be recognized. This condition may be the reason that it is not recognized by radiologists to date. Therefore, authenticating their value on the basis of a large group of patients with EOTs is important. In the past few decades, the MRI of the female pelvis has gained vast acceptance by gynecologists. In the literature, the accuracy of MR imaging in distinguishing malignant from benign complex adnexal masses ranges from 83–93% [ 24 – 28 ]. This result has been proven to be superior to CT in the assessment of complex and indeterminate ovarian tumors due to its excellent capacity for tissue characterization [ 29 ]. However, few studies have reported the structured standards for preoperative EOTs discrimination by using MR imaging. The results of our study demonstrated some differences in the clinical data and the MRI findings of the three groups. Clinically, MEOTs often involved elderly patients and a high proportion of postmenopausal patients than the two other groups. In the biochemical index examination, CA-125 is the most common screening and monitoring marker of EOTs, but its sensitivity and PPV are not ideal because it can be increased in some benign non-neoplastic diseases[ 30 ]. HE4 was low in patients with benign ovarian diseases but highly expressed in patients with MEOTs [ 31 – 33 ]. Thus, ROMA is established and studied on the basis of the CA-125 and HE4 levels and postmenopausal status [ 25 ]. In our study, MEOTs showed higher CA-125 and HE4 levels and ROMA index than BeEOTs and BEOTs, which were consistent with the results of the above reports. In the MR imaging findings, BeEOTs usually showed oligocystic, mild enhancement, and small probability of ascites. BEOTs often presented polycystic, prominent enhancement of parenchyma component, and high probability of ascites, similar to MEOTs. However, MEOTs showed bilateral involvement [ 34 ]. This phenomenon may indicate that the tumors grow on both sides or that the tumor on one side invaded the other ovary. By quantifying the weight of some MR imaging indicators, BeEOTs usually showed thin-walled cysts and no solid component. However, BEOTs often displayed irregular thickened walls and less solid portion, and MEOTs were frequently characterized as completely solid or predominantly solid mass [ 35 ]. Thus, the three groups of EOTs had some different objective characteristics on MR images. Through multivariate logistic regression analysis, four imaging indicators, namely, volume of solid portion, maximum diameter of solid portion, enhancement degrees, and peritoneal carcinomatosis, were found significant in differentiating the three groups of EOTs. The enhancement of ovarian masses depends on the delivery and retention of contrast in the lesion. The vascular supply, capillary network, and leakage of contrast into the extravascular interstitial space contribute to the accumulation of contrast within the mass and great enhancement [ 36 ]. Our results showed that with the improvement of the subtype classification of ovarian tumors, increased solid components of tumors and prominent enhancement degrees were observed, which are in line with other reports [ 37 ]. The solid portion maybe had abundant tumor vascular supply [ 38 ], damaged basement membrane, and extracellular matrix. Consequently, MEOTs displayed prominent enhancement. MEOTs metastasize intra-abdominally with often numerous, superficial, small-sized lesions. This process is called peritoneal carcinomatosis. Previous literature has shown that peritoneal carcinomatosis may occur in BEOTs, but its incidence was evidently lower than that in MEOTs, which was consistent with our findings (2.0% vs. 24.49%). Serous carcinoma, particularly high-grade serous carcinoma, often appears as peritoneal carcinomatosis [ 39 , 40 ]. The underlying mechanisms of interactions between MEOTs and peritoneal cells are incompletely understood. In addition, the mechanisms that enable tumor adhesion and growth probably involve cadherin restructuring on the epithelial ovarian cancer cells, integrin-mediated adhesion, and mesothelial evasion by mechanical forces driven by integrin–ligand interactions [ 41 ]. In terms of diagnostic performance, most quantitative indicators had a satisfactory performance and acceptable sensitivity and specificity, as shown by the multivariate analysis of MR imaging findings. The AUCs of these quantitative imaging indicators except peritoneal carcinomatosis in differentiating BeEOTs from BEOTs ranged from 0.74 to 0.853. However, the AUCs for differentiating BEOTs and MEOTs ranged from 0.579 to 0.791, indicating that the quantitative imaging measurement was useful for preoperative diagnosis and clinical decision-making. Therefore, this differentiation method can easily be generalized for use by all radiologists, regardless of their degree of expertise in pelvic imaging, as a means of improving report standardization. Several limitations were present in our study. First, some of the cases of pathological diagnosis were controversial. These cases included serous cystadenoma with focal borderline, which was categorized into BEOTs on the basis of the highest pathological grade. This practice narrowed the differences between the three groups or two groups to a certain extent. Thus, a detailed grouping and precise indicators on these tumors are necessary, which is crucial when deciding to opt for reasonable treatment [ 3 – 11 ]. Second, some cases were not performed using DW imaging and DCE-MRI in our early study. Thus, some other useful imaging features, such as ADC value and time–signal intensity curve, were not included for assessment. These factors will be considered in future research. Third, our results were based on the analysis of EOTs only and not available for other pathologic type masses, such as other types of neoplastic or non-neoplastic masses. Finally, all MR imaging examinations were performed in a single institution. The value of the indicators of these MRI features in the differentiation of three kinds of EOTs should be confirmed in a large prospective multicenter study. In conclusion, this retrospective study has shown that the data of quantitative MR imaging indices can provide an objective basis for preoperative diagnosis and clinical decision-making. Among these indices, the volume of solid portion, maximum diameter of solid portion, enhancement degrees how good diagnostic performance. This result lay the foundation in proposing a standardized nomenclature for reporting the MRI findings of adnexal masses, which is especially useful for future artificial intelligence application in this field. Abbreviations EOTs Epithelial ovarian tumors BeEOTs Benign epithelial ovarian tumors BEOTs Borderline epithelial ovarian tumors MEOTs Malignant epithelial ovarian tumors ROC Receiver-operating characteristic CA-125 Serum carbohydrate antigen 125 HE4 Human epididymis protein 4 ROMA Risk of ovarian malignancy algorithm MR Magnetic resonance AUC Area under the curve TR Repetition time TE Echo time FOV Field of view NEX Number of excitations DCE-MRI Dynamic contrast-enhanced MRI PACS Picture Archiving and Communication System IOTA International Ovarian Tumor Analysis ICCs Intraclass correlation coefficients NPV Negative predictive value PPV Positive predictive value GI-RADS Gynecologic Imaging Reporting and Data System DWI Diffusion Weighted Imaging ADC Apparent Diffusion Coefficient Declarations Ethics approval and consent to participate: Institutional Review Board approval was obtained. Consent for publication: This retrospective study was approved by the institutional review board with the waiver of the informed consent. Availability of data and materials: All data generated or analysed during this study are included in this published article. Competing interests: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Funding: This study has received funding by National Natural Science Foundation of China (81871325). Authors' contributions: Fuxia Xiao and Lin Zhang: Participated in the whole process of this study, including designed experiment, collected data, performed the data analyses and wrote the manuscript. Sihua Yang and Kun Peng: contributed to analysis and manuscript preparation. Ting Hua: contributed to the conception of the study. Guangyu Tang: contributed significantly to experiment design and revision of manuscript. 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Semiquantitative Dynamic Contrast Enhanced MRI for Accurate Classification of Complex Adnexal Masses. J Magn Reson Imaging. 2017 Feb;45(2):418-427. Li HM, Qiang JW , Ma FH, Zhao SH. The value of dynamic contrast–enhanced MRI in characterizing complex ovarian tumors. J Ovarian Res. 2017 Jan 14;10(1):4. Lidia R. Medeiros, MD, PhD; Luciana B. Freitas, MSc. Accuracy of magnetic resonance imaging in ovarian tumor: a systematic quantitative review. Am J Obstet Gynecol. 2011 Jan;204(1):67.e1-10. He Zhang, Yunfei Mao, Xiaojun Chen. Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. Eur Radiol. 2019 Jul;29(7):3358-3371. Kinkel K, Lu Y, Mehdizade A, Pelte MF, Hricak H. Indeterminate ovarian mass at US: incremental value of second imaging test for characterization-meta-analysis and Bayesian analysis. Radiology 2005;236(1):85-94. Brian D. Nicholson，Mei-Man Lee1，Dileep Wijeratne，et al. Trends in Cancer Antigen 125 testing 2003-2014: A primary care population- based cohort study using laboratory data[J] Eur J Cancer Care(Engl)2019；Jan；28（1）：e12914 Hellstrom I，Raycraft J，Hayden -Ledbetter M，et al. The HE4 （WFDC2）protein is a biomarker for ovarian carcinoma [J]. Cancer Res，2003，63（13）：3695-3700. Drapkin R，von Horsten HH，Lin Y，et al. Human epididymis protein 4 （HE4） is a secreted glycoprotein that is over expressed by serous and endometrioid ovarian carcinomas [J]. Cancer Res,2005,65(6):2162-2169. Teresa Granato, Maria Grazia Porpora, Flavia Longo. HE4 in the differential diagnosis of ovarian masses. Clin Chim Acta. 2015 Jun 15;446:147-55. Mukuda N, Fujii S, Inoue C, Fukunaga T, Oishi T, Harada T, Ogawa T. Bilateral Ovarian Tumors on MRI: How Should We Differentiate the Lesions? Yonago Acta Med. 2018 Jun 18;61(2):110-116. Denewar FA, Takeuchi M, Urano M, Kamishima Y, Kawai T, Takahashi N, Takeuchi M, Kobayashi S, Honda J, Shibamoto Y. Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis. Eur J Radiol. 2017 Jun;91:116-123. T. Jeswani and A. R. Padhani. Imaging tumour angiogenesis. Cancer Imaging, vol. 5, pp. 131-138, 2005. Thomassin-Naggara I, Bazot M, Dara ̈ı E et al (2008) Epithelial ovarian tumors: value of dynamic contrast-enhanced MR imaging and correlation with tumor angiogenesis. Radiology 248:148-159 S.H. Zhao, J.W. Qiang, G.F. Zhang, et al., Diffusion-weighted MR imaging for dif- ferentiating borderline from malignant epithelial tumours of the ovary: pathological correlation, Eur. Radiol. 24 (2014) 2292–2299. Morita H, Aoki J, Taketomi A, Sato N, Endo K. Serous sur- face papillary carcinoma of the peritoneum: clinical, radiolog- ic, and pathologic findings in 11 patients. AJR. 2004;183:923-8. Tanaka YO, Okada S, Satoh T, Matsumoto K, Oki A, Saida T, et al. Differentiation of epithelial ovarian cancer subtypes by use of imaging and clinical data: a detailed analysis. Cancer Imaging. 2016;16:3. PMID: 26873307. van Baal JOAM, van Noorden CJF, Nieuwland R, Van de Vijver KK, Sturk A, van Driel WJ, Kenter GG, Lok CAR. Development of Peritoneal Carcinomatosis in Epithelial Ovarian Cancer : A Review. J Histochem Cytochem. 2018 Feb;66(2):67-83. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 06 Aug, 2021 Review # 1 received at journal 28 Apr, 2021 Reviewer # 1 agreed at journal 20 Apr, 2021 Editor assigned by journal 19 Apr, 2021 Reviewers invited by journal 19 Apr, 2021 Submission checks completed at journal 19 Apr, 2021 Editor invited by journal 19 Apr, 2021 First submitted to journal 20 Mar, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-350729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":22457925,"identity":"4ab535db-93cc-4d13-9f3c-c712e288e027","order_by":0,"name":"Fuxia Xiao","email":"","orcid":"","institution":"Tongji University Tenth People's Hospital: Shanghai Tenth People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fuxia","middleName":"","lastName":"Xiao","suffix":""},{"id":22457926,"identity":"75951335-316b-4816-9176-63a2c6ad2c5e","order_by":1,"name":"Lin Zhang","email":"","orcid":"","institution":"Tongji University Tenth People's Hospital: Shanghai Tenth People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhang","suffix":""},{"id":22457927,"identity":"4774b506-4a10-4702-8c8a-594061d5d14b","order_by":2,"name":"Sihua Yang","email":"","orcid":"","institution":"Tongji University Tenth People's Hospital: Shanghai Tenth People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sihua","middleName":"","lastName":"Yang","suffix":""},{"id":22457928,"identity":"20e6b503-6e27-433d-8418-d66e4b36b9ac","order_by":3,"name":"Kun Peng","email":"","orcid":"","institution":"Tongji University Tenth People's Hospital: Shanghai Tenth People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Peng","suffix":""},{"id":22457929,"identity":"c60664eb-50c4-4e48-b933-97d7a06f6bee","order_by":4,"name":"Ting Hua","email":"","orcid":"","institution":"Tongji University Tenth People's Hospital: Shanghai Tenth People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Hua","suffix":""},{"id":22457930,"identity":"937235af-b620-44a8-9716-66a7cbc8c562","order_by":5,"name":"Guangyu Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYJCCw38qwHTCAQYGZsLKeRgYGA/wnDEgTQvzAd42AxifCC32/GsMDkjO+yNnzr/g4QGGCuvEBvazB/DbIvHG4IDhNgNjyxkPgA47k57YwJOXQEDLGYMDidsMEjfcOJBwgLHtcGKDBI8BYS0H58C0/CNGC3+PwcHGBqCW8w1ALQ3EaLnBVnCY4ZixscENYCAnHEs3buPJwa+Fvf/w5s8MNXJyBufPJH/4UGMt289+Br8WBokMqAKJnASGBCDNhl89EPAffwBjHCCoeBSMglEwCkYmAADNME6JtXapCwAAAABJRU5ErkJggg==","orcid":"","institution":"Tongji University Tenth People's Hospital: Shanghai Tenth People's Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guangyu","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2021-03-21 16:58:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-350729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-350729/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8330549,"identity":"6853f079-5181-4936-a7c1-705c383475e3","added_by":"auto","created_at":"2021-04-22 14:36:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":503924,"visible":true,"origin":"","legend":"A 74-year-old woman with right serous cystadenoma. (A–B) Tumor with few loculi shows low and high signal intensities on T1WI and T2WI, respectively. The pelvis region has no peritoneal carcinomatosis and ascite. The thin wall and septum (arrows) in contrast-enhanced T1WI (C–D) exhibit mild enhancement.","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-350729/v1/f3fcaab096394e765ba63990.jpg"},{"id":8330880,"identity":"d7c53a54-d2ac-4e64-b70c-68a5d7aa875a","added_by":"auto","created_at":"2021-04-22 14:39:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":468344,"visible":true,"origin":"","legend":"A 25-year-old woman with right mucinous borderline neoplasm. (A–B) Multilocular cystic mass with mild thickened capsule wall on the axial T1W and T2W images in the pelvis (arrow). (C–D) Prominent enhancement of the unevenly thickened capsule wall and septum on axial and sagittal contrast-enhanced T1W images with FS (arrows). ","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-350729/v1/cc5c865ff992e7dce2eb4700.jpg"},{"id":8330547,"identity":"5625df27-4090-469f-90c2-2fe3f13e7e13","added_by":"auto","created_at":"2021-04-22 14:36:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":487181,"visible":true,"origin":"","legend":"A 52-year-old woman with bilateral high grade of serous ovarian carcinoma. (A–B) Irregular solid mass on the bilateral ovarian regions with unclear boundaries present isointensity and slight hyperintensity signals on axial T1WI and T2WI, respectively. Ascite in rectum lacuna (pentastar) was found. (C–D) Axial and coronary contrast-enhanced fat-suppressed T1-weighted MR image shows markedly and unevenly enhanced solid component within complex solid and follicular mass in pelvis (arrows).","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-350729/v1/8bff9ecb44989544ed56cd7e.jpg"},{"id":8330879,"identity":"3d6dfed0-3917-42eb-bdd0-9aff037eae1f","added_by":"auto","created_at":"2021-04-22 14:39:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1439641,"visible":true,"origin":"","legend":"Receiver operating characteristic (ROC) curve analysis of MR imaging parameters, including volume of solid portion, maximum diameter of solid portion, enhancement degrees, peritoneal carcinomatosis, and their combination for discriminating BeEOTs and BEOTs (A) and BEOTs and MEOTs (B).","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-350729/v1/293c9d5434c0f380a16c1fbc.jpg"},{"id":13687852,"identity":"b7ed41b0-f8a7-4431-9d03-cef57795df2c","added_by":"auto","created_at":"2021-09-17 12:22:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":833753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-350729/v1/e04e8687-87c7-4036-911a-4f3d6fbe77d8.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eQuantitative Analysis of the MRI Features in the Differentiation of Benign, Borderline, and Malignant Epithelial Ovarian Tumors\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eEpithelial ovarian tumor (EOT) is the most common type in the classification of ovarian tumors and are categorized as benign (BeEOTs), borderline (BEOTs), and malignant (MEOTs) on the basis of histological results. Ovarian tumors remain the first indication for gynecologic surgery [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Laparoscopic tumor exfoliation or unilateral ovariectomy can be performed if the mass is a small BeEOT [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Young patients with BEOTs can undergo conservative surgery to preserve fertility or maintain ovarian function [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], whereas patients with MEOTs require the radical resection of tumors, followed by adjuvant chemotherapy [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, the accurate diagnosis of the preoperative subtype of EOTs is important for the patient\u0026rsquo;s therapeutic schedule and prognosis. This study aims to analyze the quantitative indicators of magnetic resonance (MR) image for the accurate diagnosis of EOTs and explore the weight of those features in the differential diagnoses of the three types of EOTs through multiple regression analysis.\u003c/p\u003e "},{"header":"Material And Methods","content":"\u003ch2\u003ePatients\u003c/h2\u003e\n\u003cp\u003eAll patients with EOTs who underwent preoperative MRI from our picture archiving and communication system (PACS) database and had pathological results between January 1, 2009 and August 31, 2018 were retrospectively recruited. The subjects consisted of 477 patients with 513 EOTs. A total of 441 women had one mass, and 36 women had two masses. The population characteristics and biochemical examinations are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The recruit tumors were categorized into the BeEOTs, BEOTs, and MEOTs groups on the basis of the pathological results. This retrospective study was approved by the institutional review board with the waiver of the informed consent.\u003c/p\u003e\n\u003ch2\u003eMRI technique\u003c/h2\u003e\n\u003cp\u003eThe MR images were acquired using the 3.0-T MR imaging unit (Magnetom Verio, Siemens Medical Solutions, Germany) by employing a pelvic phased-array coil. The following imaging sequences were performed: transverse nonfat-suppressed T2-weighted turbo spin-echo sequences (repetition time [TR], 4050 ms; echo time [TE], 84 ms; section thickness, 4 mm; field of view (FOV), 325 mm; matrix, 384 \u0026times; 256; and number of excitations [NEX] 2), transverse nonfat-suppressed T1-weighted gradient-echo sequences (TR, 550 ms; TE, 13 ms; section thickness, 4 mm; FOV, 325 mm; matrix, 384 \u0026times; 256; and NEX, 2), sagittal fat-suppressed T2-weighted turbo spin-echo sequences, and coronal nonfat-suppressed T2-weighted turbo spin-echo sequences. Then, dynamic contrast-enhanced MRI (DCE-MRI) with 3D fat-suppressed T1-weighted interpolated spoiled gradient-echo sequence with volumetric interpolated breath-hold examination was performed in the transverse, sagittal, and coronal planes at scanning delay times of 40 and 120 s after the bolus injection (2.5 mL/s) of gadopentetate dimeglumine (0.5 mol/L, Beijing Beilu Pharmaceutical Company) at a dose of 0.1 mmol/kg, followed by 50 mL saline flush through the antecubital vein.\u003c/p\u003e\n\u003ch2\u003eMR images analysis\u003c/h2\u003e\n\u003cp\u003eTwo radiologists who were blinded to the pathological results independently reviewed the MR images and collected the clinical information of the patients. The characteristics of MRI include volume of tumor, maximum diameter of tumor, septum thickness, volume of solid portion, volume ratio of solid portion, maximum diameter of solid portion, maximum diameter ratio of solid portion, number of cysts, peritoneal carcinomatosis, ascites, bilateral involvemen. The criteria of MRI were elaborated on the basis of several previously published terms (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDefinition of MRI findings\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTerm\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDefinition\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMeasurement standard\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeptum thickness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTimmerman et al[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThickness of septum or septa within a cystic tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIf the septum is irregular, select the thickest focal area.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAO Mei-chun[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe volume of tumors was estimated in PACS by measuring the area of the tumor on contiguous 3.0 mm thick transverse slices throughout the whole length of tumor by using manually drawn boundaries. The area was generated automatically, and the volume of tumors were calculated by multiplying the slice thickness with the sum of the tumor cross-sectional area (Cavalieri\u0026rsquo;s principle)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTimmerman et al[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAs defined by the IOTA group, at MR imaging, solid tissue enhances after gadolinium chelate injection. Therefore, the solid tissue includes vegetation.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe method of measurement was the same as that of the \u0026ldquo;volume of tumor\u0026rdquo;.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe proportion of solid components in the total tumorous volume\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e=Volume of solid portion/Volume of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe diameter of the largest level of the tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe diameter of the largest level of the tumorous solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe ratio of the maximum diameter of solid portion and the maximum diameter of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e=Maximum diameter of solid portion/Maximum diameter of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analysis was performed using the SPSS 20.0 (SPSS, Inc., Chicago, IL, USA). Continuous variables, such as patient\u0026rsquo;s age and serum carbohydrate antigen 125 (CA-125) level, were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The kappa and intraclass correlation (ICCs) coefficients were calculated to assess the interobserver agreement between the two readers for imaging parameter analysis. A kappa value of 0.00\u0026ndash;0.20, 0.21\u0026ndash;0.40, 0.41\u0026ndash;0.60, 0.61\u0026ndash;0.80, and 0.81\u0026ndash;1.00 indicated slight, fair, moderate, substantial, and almost perfect agreement, respectively[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. An ICC value of 0.00\u0026ndash;0.10 indicated virtually no agreement, and ICC values of 0.11\u0026ndash;0.40, 0.41\u0026ndash;0.60, 0.61\u0026ndash;0.80, and 0.81\u0026ndash;1.00 indicated slight, fair, moderate, and substantial agreement, respectively[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. In order to identify significant differences in MR imaging parameters, population characteristics and biochemical examinations, the Kruskal-Wallis test was used for continuous variables and categorical data among three groups. Multivariate logistic regression analysis was performed using all qualitative and quantitative variables to find the independent diagnostic factors. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of MR characteristics and clinical information in differentiating BeEOTs, BEOTs, and MEOTs. ROC analysis was performed using the Medcalc version 15.6 (MedCalc Software, Mariakerke, Belgium). A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eClinical evaluation\u003c/h2\u003e\n\u003cp\u003eThe population characteristics and biochemical examinations of the blood samples of 477 patients with 513 ovarian masses are demonstrated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Their mean age was 52.36\u0026thinsp;\u0026plusmn;\u0026thinsp;12.71 (range 18\u0026ndash;86) years. A total of 208 (43.61%) women were premenopausal, and 269 (56.39%) were postmenopausal. Thirty-six (7.55%) patients had bilateral tumors, and 441 (92.45%) patients had unilateral tumors. The significant differences were obtained for all indicators, including age, postmenopause, CA-125 level, human epididymis protein 4 (HE4), and premenopausal and postmenopausal risk of ovarian malignancy algorithm (ROMA) indices.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePopulation clinical characteristics and biochemical examinations of blood\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBeEOTs(n\u0026thinsp;=\u0026thinsp;330,\u003c/p\u003e\n\u003cp\u003en\u003csup\u003e*\u003c/sup\u003e=347, n\u003csup\u003e※\u003c/sup\u003e=305)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBEOTs(n\u0026thinsp;=\u0026thinsp;49,\u003c/p\u003e\n\u003cp\u003en\u003csup\u003e*\u003c/sup\u003e=50, n\u003csup\u003e※\u003c/sup\u003e=48)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMEOTs(n\u0026thinsp;=\u0026thinsp;98,\u003c/p\u003e\n\u003cp\u003en\u003csup\u003e*\u003c/sup\u003e=116, n\u003csup\u003e※\u003c/sup\u003e=109)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.20\u0026thinsp;\u0026plusmn;\u0026thinsp;13.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.61\u0026thinsp;\u0026plusmn;\u0026thinsp;17.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostmenopausal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148(44.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26(53.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(25.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e182(55.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(46.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73(74.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCA-125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.92\u0026thinsp;\u0026plusmn;\u0026thinsp;29.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.82\u0026thinsp;\u0026plusmn;\u0026thinsp;191.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e523.92\u0026thinsp;\u0026plusmn;\u0026thinsp;835.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHE 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.92\u0026thinsp;\u0026plusmn;\u0026thinsp;16.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.12\u0026thinsp;\u0026plusmn;\u0026thinsp;124.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e260.23\u0026thinsp;\u0026plusmn;\u0026thinsp;239.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePremenopausal ROMA index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.36\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.81\u0026thinsp;\u0026plusmn;\u0026thinsp;35.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostmenopausal ROMA index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.06\u0026thinsp;\u0026plusmn;\u0026thinsp;15.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.95\u0026thinsp;\u0026plusmn;\u0026thinsp;30.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe case number of BeEOTs (n) is 330 ( unilateral 313, bilateral 17) with 347 tumors (n\u003csup\u003e*\u003c/sup\u003e). The number of tumors with contrast enhanced MR imaging(n\u003csup\u003e※\u003c/sup\u003e) is 305.\u003c/p\u003e\n\u003cp\u003eThe case number of BEOTs (n) is 49 ( unilateral 48, bilateral 1) with 50 tumors (n\u003csup\u003e\u003cspan style=\"font-size: xx-small;\"\u003e*\u003c/span\u003e\u003c/sup\u003e). The number of tumors with contrast enhanced MR imaging (n\u003csup\u003e※\u003c/sup\u003e) is 48.\u003c/p\u003e\n\u003cp\u003eThe case number of MEOTs (n) is 98 ( unilateral 80, bilateral 18) with 116 tumors (n\u003csup\u003e\u003cspan style=\"font-size: xx-small;\"\u003e*\u003c/span\u003e\u003c/sup\u003e). The number of tumors with contrast enhanced MR imaging (n\u003csup\u003e※\u003c/sup\u003e) is 109.\u003c/p\u003e\n\u003cp\u003eThe number in parenthesis is the percentage.\u003c/p\u003e\n\u003ch2\u003eInterobserver Agreement\u003c/h2\u003e\n\u003cp\u003eFor all MR imaging variables, the interobserver agreement was good (ICC\u0026thinsp;=\u0026thinsp;0.899\u0026thinsp;\u0026minus;\u0026thinsp;0.999, kappa\u0026thinsp;=\u0026thinsp;0.932\u0026thinsp;\u0026minus;\u0026thinsp;0.978; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eInterobserver agreement of MR imaging variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMR Imaging Variables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026Kappa; value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eICC(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.988(0.985\u0026ndash;0.991)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.899(0.870\u0026ndash;0.922)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.982(0.976\u0026ndash;0.986)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.988(0.985\u0026ndash;0.991)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.995(0.994\u0026ndash;0.997)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999(0.998\u0026ndash;0.999)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnhancement degrees\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAscites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeritoneal carcinomatosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.932\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eMR image analysis\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the characteristics of the MR imaging findings in EOTs among benign, borderline, and malignant lesions by using univariate analysis. Compared with BEOTs, BeEOTs and MEOTs had less cysts (23/50, 46 % vs 311/347, 89.63 % and 88/116, 75.86 %, \u003cem\u003eP\u0026thinsp;\u003c/em\u003e\u0026lt;\u0026thinsp;0.001). Most BeEOTs had mild enhancement (290/305, 95.08 % vs 16/48, 33.33 % and 9/109, 8.26 %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and less frequent ascites (75/330, 22.72 % vs 27/49, 55.10 % and 65/98, 66.33%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) than BEOTs and MEOTs. Peritoneal carcinomatosis was found in 24.49 % (24/98) of MEOTs, 0% of BeEOTs, and 2.04% (1/49) of BEOTs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and bilateral involvement were more frequent in MEOTs (18.37%, 18/98) than in BeEOTs (6.06 %, 20/330) and BEOTs (2.04%, 1/49) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In quantitative MR imaging descriptors, BeEOTs usually showed thin-walled cysts and no solid component, but BEOTs often displayed irregular thickened walls and small amount of solid portion. MEOTs were more frequently characterized as completely solid or predominantly solid mass (\u003cem\u003eP\u0026thinsp;\u003c/em\u003e\u0026lt;\u0026thinsp;0.001, Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). No statistical difference was found among the three groups in terms of volume of tumor and maximum diameter of tumor(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055, respectively).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe difference of MRI parameters among three groups of EOTs\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBeEOTs(n\u0026thinsp;=\u0026thinsp;330,\u003c/p\u003e\n\u003cp\u003en\u003csup\u003e*\u003c/sup\u003e=347, n\u003csup\u003e※\u003c/sup\u003e=305)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBEOTs(n\u0026thinsp;=\u0026thinsp;49,\u003c/p\u003e\n\u003cp\u003en\u003csup\u003e*\u003c/sup\u003e=50, n\u003csup\u003e※\u003c/sup\u003e=48)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMEOTs(n\u0026thinsp;=\u0026thinsp;98,\u003c/p\u003e\n\u003cp\u003en\u003csup\u003e*\u003c/sup\u003e=116, n\u003csup\u003e※\u003c/sup\u003e=109)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeptum thickness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e483.30\u0026thinsp;\u0026plusmn;\u0026thinsp;883.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1106.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2000.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e412.88\u0026thinsp;\u0026plusmn;\u0026thinsp;674.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.23\u0026thinsp;\u0026plusmn;\u0026thinsp;163.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.63\u0026thinsp;\u0026plusmn;\u0026thinsp;120.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.58\u0026thinsp;\u0026plusmn;\u0026thinsp;19.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.36\u0026thinsp;\u0026plusmn;\u0026thinsp;37.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.02\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.84\u0026thinsp;\u0026plusmn;\u0026thinsp;30.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.56\u0026thinsp;\u0026plusmn;\u0026thinsp;33.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBilateral involvement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e310(93.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48(97.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80(81.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20(6.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(2.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(18.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of cysts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e311(89.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(46.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88(75.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026ndash;10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(4.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(6.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13(11.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(6.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(48.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(12.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnhancement degrees\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e290(95.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16(33.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(8.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(8.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29(26.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProminent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(4.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28(58.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71(65.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAscites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e255(77.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22(44.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33(33.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75(22.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(55.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65(66.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeritoneal carcinomatosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e330(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48(97.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74(75.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(2.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(24.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis was performed to obtain independent differential diagnostic factors. Results are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The outcome revealed that volume of solid portion(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), maximum diameter of solid portion(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), enhancement degrees(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and peritoneal carcinomatosis(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) were significant indicators for differentiate diagnosis of the three groups.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate Logistic Regression of MR imaging parameters in EOTs\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCovariate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRegression coefficient\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWald\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostmenopausal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.851\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.597\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.695\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.453\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.038\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter ratio of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBilateral involvement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.881\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.492\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of cysts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.930\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnhancement degrees\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.289\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAscites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.329\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeritoneal carcinomatosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003e* \u003c/sup\u003eindicate a significant difference among three groups.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThen, the diagnostic performance of MR imaging parameters, including volume of solid portion, maximum diameter of solid portion, enhancement degrees, peritoneal carcinomatosis, and their combination were assessed and compared using ROC analyses to differentiate two groups.\u003c/p\u003e\n\u003cp\u003eThe area under the curve(AUC), sensitivity, specificity, positive predictive value(PPV) and negative predictive value(NPV) of this multivariate logistic regression model are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In comparing BeEOTs and BEOTs, the image features of volume of solid portion, maximum diameter of solid portion, enhancement degrees, and the combination of four image features revealed moderate diagnostic value (0.74, 0.74, 0.8, 0.85, respectively), whereas peritoneal carcinomatosis showed low diagnostic value (0.51). Moreover, the above indicators except enhancement degrees (0.58) and peritoneal carcinomatosis(0.61) demonstrated moderate diagnostic value in BEOTs and MEOTs (0.78, 0.76,0.79, respectively).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReceiver operating characteristic analysis of MR imaging parameters\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esensitivity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003especificity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePPV(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNPV(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVolume of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeEOTs vs BEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBEOTs vs MEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e63.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of solid portion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeEOTs vs BEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBEOTs vs MEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEnhancement degrees\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeEOTs vs BEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBEOTs vs MEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePeritoneal carcinomatosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeEOTs vs BEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBEOTs vs MEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCombination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeEOTs vs BEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e74.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBEOTs vs MEOTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAdnexal masses, in general, are first found and evaluated using ultrasonography[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, in a prospective randomized trial in 2010, a consensus conference of the Society of Radiologists in Ultrasound proposed that establishing structured standards for adnexal cysts is needed[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. To date, numerous scoring systems for preoperative mass discrimination have been developed[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Fernando Amor et al.[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] proposed the Gynecologic Imaging Reporting and Data System (GI-RADS) to guide every imaging modality in describing and categorizing ovarian lesions in ultrasonography, but they did not specify the basis of classification and the imaging evidence for each category crucial to be recognized. This condition may be the reason that it is not recognized by radiologists to date. Therefore, authenticating their value on the basis of a large group of patients with EOTs is important.\u003c/p\u003e\n\u003cp\u003eIn the past few decades, the MRI of the female pelvis has gained vast acceptance by gynecologists. In the literature, the accuracy of MR imaging in distinguishing malignant from benign complex adnexal masses ranges from 83\u0026ndash;93% [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. This result has been proven to be superior to CT in the assessment of complex and indeterminate ovarian tumors due to its excellent capacity for tissue characterization [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, few studies have reported the structured standards for preoperative EOTs discrimination by using MR imaging.\u003c/p\u003e\n\u003cp\u003eThe results of our study demonstrated some differences in the clinical data and the MRI findings of the three groups. Clinically, MEOTs often involved elderly patients and a high proportion of postmenopausal patients than the two other groups. In the biochemical index examination, CA-125 is the most common screening and monitoring marker of EOTs, but its sensitivity and PPV are not ideal because it can be increased in some benign non-neoplastic diseases[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. HE4 was low in patients with benign ovarian diseases but highly expressed in patients with MEOTs [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Thus, ROMA is established and studied on the basis of the CA-125 and HE4 levels and postmenopausal status [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. In our study, MEOTs showed higher CA-125 and HE4 levels and ROMA index than BeEOTs and BEOTs, which were consistent with the results of the above reports.\u003c/p\u003e\n\u003cp\u003eIn the MR imaging findings, BeEOTs usually showed oligocystic, mild enhancement, and small probability of ascites. BEOTs often presented polycystic, prominent enhancement of parenchyma component, and high probability of ascites, similar to MEOTs. However, MEOTs showed bilateral involvement [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. This phenomenon may indicate that the tumors grow on both sides or that the tumor on one side invaded the other ovary. By quantifying the weight of some MR imaging indicators, BeEOTs usually showed thin-walled cysts and no solid component. However, BEOTs often displayed irregular thickened walls and less solid portion, and MEOTs were frequently characterized as completely solid or predominantly solid mass [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. Thus, the three groups of EOTs had some different objective characteristics on MR images.\u003c/p\u003e\n\u003cp\u003eThrough multivariate logistic regression analysis, four imaging indicators, namely, volume of solid portion, maximum diameter of solid portion, enhancement degrees, and peritoneal carcinomatosis, were found significant in differentiating the three groups of EOTs. The enhancement of ovarian masses depends on the delivery and retention of contrast in the lesion. The vascular supply, capillary network, and leakage of contrast into the extravascular interstitial space contribute to the accumulation of contrast within the mass and great enhancement [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our results showed that with the improvement of the subtype classification of ovarian tumors, increased solid components of tumors and prominent enhancement degrees were observed, which are in line with other reports [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. The solid portion maybe had abundant tumor vascular supply [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], damaged basement membrane, and extracellular matrix. Consequently, MEOTs displayed prominent enhancement. MEOTs metastasize intra-abdominally with often numerous, superficial, small-sized lesions. This process is called peritoneal carcinomatosis. Previous literature has shown that peritoneal carcinomatosis may occur in BEOTs, but its incidence was evidently lower than that in MEOTs, which was consistent with our findings (2.0% vs. 24.49%). Serous carcinoma, particularly high-grade serous carcinoma, often appears as peritoneal carcinomatosis [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The underlying mechanisms of interactions between MEOTs and peritoneal cells are incompletely understood. In addition, the mechanisms that enable tumor adhesion and growth probably involve cadherin restructuring on the epithelial ovarian cancer cells, integrin-mediated adhesion, and mesothelial evasion by mechanical forces driven by integrin\u0026ndash;ligand interactions [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn terms of diagnostic performance, most quantitative indicators had a satisfactory performance and acceptable sensitivity and specificity, as shown by the multivariate analysis of MR imaging findings. The AUCs of these quantitative imaging indicators except peritoneal carcinomatosis in differentiating BeEOTs from BEOTs ranged from 0.74 to 0.853. However, the AUCs for differentiating BEOTs and MEOTs ranged from 0.579 to 0.791, indicating that the quantitative imaging measurement was useful for preoperative diagnosis and clinical decision-making. Therefore, this differentiation method can easily be generalized for use by all radiologists, regardless of their degree of expertise in pelvic imaging, as a means of improving report standardization.\u003c/p\u003e\n\u003cp\u003eSeveral limitations were present in our study. First, some of the cases of pathological diagnosis were controversial. These cases included serous cystadenoma with focal borderline, which was categorized into BEOTs on the basis of the highest pathological grade. This practice narrowed the differences between the three groups or two groups to a certain extent. Thus, a detailed grouping and precise indicators on these tumors are necessary, which is crucial when deciding to opt for reasonable treatment [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Second, some cases were not performed using DW imaging and DCE-MRI in our early study. Thus, some other useful imaging features, such as ADC value and time\u0026ndash;signal intensity curve, were not included for assessment. These factors will be considered in future research. Third, our results were based on the analysis of EOTs only and not available for other pathologic type masses, such as other types of neoplastic or non-neoplastic masses. Finally, all MR imaging examinations were performed in a single institution. The value of the indicators of these MRI features in the differentiation of three kinds of EOTs should be confirmed in a large prospective multicenter study.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this retrospective study has shown that the data of quantitative MR imaging indices can provide an objective basis for preoperative diagnosis and clinical decision-making. Among these indices, the volume of solid portion, maximum diameter of solid portion, enhancement degrees how good diagnostic performance. This result lay the foundation in proposing a standardized nomenclature for reporting the MRI findings of adnexal masses, which is especially useful for future artificial intelligence application in this field.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEOTs Epithelial ovarian tumors\u003c/p\u003e\n\u003cp\u003eBeEOTs Benign epithelial ovarian tumors\u003c/p\u003e\n\u003cp\u003eBEOTs Borderline epithelial ovarian tumors\u003c/p\u003e\n\u003cp\u003eMEOTs Malignant epithelial ovarian tumors\u003c/p\u003e\n\u003cp\u003eROC Receiver-operating characteristic\u003c/p\u003e\n\u003cp\u003eCA-125 Serum carbohydrate antigen 125\u003c/p\u003e\n\u003cp\u003eHE4 Human epididymis protein 4\u003c/p\u003e\n\u003cp\u003eROMA Risk of ovarian malignancy algorithm\u003c/p\u003e\n\u003cp\u003eMR Magnetic resonance\u003c/p\u003e\n\u003cp\u003eAUC Area under the curve\u003c/p\u003e\n\u003cp\u003eTR Repetition time\u003c/p\u003e\n\u003cp\u003eTE Echo time\u003c/p\u003e\n\u003cp\u003eFOV Field of view\u003c/p\u003e\n\u003cp\u003eNEX Number of excitations\u003c/p\u003e\n\u003cp\u003eDCE-MRI Dynamic contrast-enhanced MRI\u003c/p\u003e\n\u003cp\u003ePACS Picture Archiving and Communication System\u003c/p\u003e\n\u003cp\u003eIOTA International Ovarian Tumor Analysis\u003c/p\u003e\n\u003cp\u003eICCs Intraclass correlation coefficients\u003c/p\u003e\n\u003cp\u003eNPV Negative predictive value\u003c/p\u003e\n\u003cp\u003ePPV Positive predictive value\u003c/p\u003e\n\u003cp\u003eGI-RADS Gynecologic Imaging Reporting and Data System\u003c/p\u003e\n\u003cp\u003eDWI Diffusion Weighted Imaging\u003c/p\u003e\n\u003cp\u003eADC Apparent Diffusion Coefficient\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board approval was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the institutional review board with the waiver of the informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received funding by National Natural Science Foundation of China (81871325).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuxia Xiao and Lin Zhang: Participated in the whole process of this study, including designed experiment, collected data, performed the data analyses and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eSihua Yang and Kun Peng: contributed to analysis and manuscript preparation.\u003c/p\u003e\n\u003cp\u003eTing Hua: contributed to the conception of the study.\u003c/p\u003e\n\u003cp\u003eGuangyu Tang: contributed significantly to experiment design and revision of manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Shanghai Tenth People\u0026rsquo;s Hospital and National Natural Science Foundation of China (81871325). The authors are grateful to all participants for their contribution to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCobb LP,\u0026nbsp; Gershenson DM. Treatment of Rare Epithelial Ovarian Tumors. Hematol. Oncol. Clin. North Am. 2018 12;32(6).\u003c/li\u003e\n\u003cli\u003eEskander R, Berman M, Keder L. Practice Bulletin No. 174: Evaluation and Management of Adnexal Masses. American College of Obstetricians and Gynecologists\u0026rsquo; Committee on Practice Bulletins\u0026mdash;Gynecology. Obstet Gynecol. 2016 Nov;128(5):e210-e226.\u003c/li\u003e\n\u003cli\u003eFeng Liang, MM, Xia Xu, MB, Bing Liang, MD. Comparison of Intraoperative Indicators and Postoperative Efficacy in Treatment of Benign Ovarian Tumor: Laparoscopy Versus Open Surgery .Am J Ther. 2017 Nov/Dec;24(6):e681-e688.\u003c/li\u003e\n\u003cli\u003eA. Guillaume, O. Pirrello.Preservation of fertility in surgery of benign and borderline malignant ovarian tumors. J Visc Surg. 2018 Jun;155 Suppl 1:S17-S21.\u003c/li\u003e\n\u003cli\u003eOzlem Dural MD , Cenk Yasa MD, Ercan Bastu MD. Laparoscopic Outcomes of Adnexal Surgery in Older Children and Adolescents. J Pediatr Adolesc Gynecol. 2017 Feb;30(1):128-131.\u003c/li\u003e\n\u003cli\u003eTakaharu Oue, Shuichiro Uehara, Takashi Sasaki. Treatment and ovarian preservation in children with ovarian tumors. J Pediatr Surg. 2015 Dec;50(12):2116-8.\u003c/li\u003e\n\u003cli\u003eF. Tomao, F. Peccatori, L. Del Pup, et al., Special issues in fertility preservation for gynecologic malignancies, Crit. Rev. Oncol. Hematol. 97 (January) (2016) 206\u0026ndash;219.\u003c/li\u003e\n\u003cli\u003eA. du Bois, F. Trillsch, S. Mahner, F. Heitz1 \u0026amp; P. Harter. Management of borderline ovarian tumors. Ann Oncol. 2016 Apr;27 Suppl 1:i20-i22.\u003c/li\u003e\n\u003cli\u003eC. Uzan, A. Kane, A. Rey, S. Gouy, P. Duvillard, P. Morice.Outcomes after conservative treatment of advanced-stage serous borderline tumors of the ovary. Ann Oncol. 2010 Jan;21(1):55-60.\u003c/li\u003e\n\u003cli\u003eZanetta G, Rota S, Chiari S, et al. Behavior of borderline tumors with particular interest to persistence, recurrence, and progression to invasive carcinoma: a prospective study. J Clin Oncol. 2001;19:2658\u0026ndash;2664.\u003c/li\u003e\n\u003cli\u003eBrian Orr, MD, Robert P. Edwards, MD. Diagnosis and Treatment of Ovarian Cancer. Hematol Oncol Clin North Am. 2018 Dec;32(6):943-964.\u003c/li\u003e\n\u003cli\u003eE. Pujade-Lauraine. New treatments in ovarian cancer. Ann Oncol. 2017 Nov 1;28(suppl_8):viii57-viii60.\u003c/li\u003e\n\u003cli\u003eJelovac D, Armstrong DK. Recent progress in the diagnosis and treatment of ovarian cancer. Ca A Cancer J Clin. 2011;61:183\u0026ndash;203.\u003c/li\u003e\n\u003cli\u003eShannon Armbruster, MD, MPHa, Robert L. Coleman, MDa,Jose Alejandro Rauh-Hain, MD, MPH. Management and Treatment of Recurrent Epithelial Ovarian Cancer. Hematol Oncol Clin North Am. 2018 Dec;32(6):965-982.\u003c/li\u003e\n\u003cli\u003eTimmerman D, Valentin L, Bourne TH, et al. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) Group. Ultrasound Obstet Gynecol. 2000; 16(5): 500-505.\u003c/li\u003e\n\u003cli\u003eGao MC, Lu QC, Li YS, et al. Study on hippocampal volume with quantitative 3T magnetic resonance imaging in Chinese patients with epilepsy. Chin Med J (Engl). 2012 Sep;125(18):3217-322.\u003c/li\u003e\n\u003cli\u003eJ.R. Landis, G.G. Koch, The measurement of observer agreement for categorical data, Biometrics 33 (1) (1977) 159\u0026ndash;174.\u003c/li\u003e\n\u003cli\u003eShrout PE. Measurement reliability and agreement in psychiatry. Stat Methods Med Res. 1998 Sep;7(3):301-17.\u003c/li\u003e\n\u003cli\u003evan Nagell JR Jr, Miller RW. 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Yonago Acta Med. 2018 Jun 18;61(2):110-116.\u003c/li\u003e\n\u003cli\u003eDenewar FA, Takeuchi M, Urano M, Kamishima Y, Kawai T, Takahashi N, Takeuchi M, Kobayashi S, Honda J, Shibamoto Y. Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis. Eur J Radiol. 2017 Jun;91:116-123.\u003c/li\u003e\n\u003cli\u003eT. Jeswani and A. R. Padhani. Imaging tumour angiogenesis. Cancer Imaging, vol. 5, pp. 131-138, 2005.\u003c/li\u003e\n\u003cli\u003eThomassin-Naggara I, Bazot M, Dara ̈ı E et al (2008) Epithelial ovarian tumors: value of dynamic contrast-enhanced MR imaging and correlation with tumor angiogenesis. Radiology 248:148-159\u003c/li\u003e\n\u003cli\u003eS.H. Zhao, J.W. Qiang, G.F. Zhang, et al., Diffusion-weighted MR imaging for dif- ferentiating borderline from malignant epithelial tumours of the ovary: pathological correlation, Eur. 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J Histochem Cytochem. 2018 Feb;66(2):67-83.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ovarian Neoplasms, Magnetic resonance imaging, Differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-350729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-350729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aims to investigate the value of the quantitative indicators of MRI in the differential diagnoses of benign, borderline, and malignant epithelial ovarian tumors (EOTs).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e: The study population comprised 477 women with 513 masses who underwent MRI and operation, including benign EOTs (BeEOTs), borderline EOTs (BEOTs), and malignant EOTs (MEOTs). The clinical information and MRI findings of the three groups were compared. Then, multivariate logistic regression analysis was performed to find the independent diagnostic factors. The receiver operating characteristic (ROC) curves were also used to evaluate the diagnostic performance of the quantitative indicators of MRI and clinical information in differentiating BeEOTs from BEOTs or differentiating BEOTs from MEOTs.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The MEOTs likely involved postmenopausal women and showed higher CA-125, HE4 levels, ROMA indices, peritoneal carcinomatosis and bilateral involvement than BeEOTs and BEOTs. Compared with BEOTs, BeEOTs and MEOTs appeared to be more frequently oligocystic (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001). BeEOTs were more likely to show mild enhancement (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001) and less ascites (\u003cem\u003eP \u003c/em\u003e= 0.003) than BEOTs and MEOTs. In the quantitative indicators of MRI, BeEOTs usually showed thin-walled cysts and no solid component. BEOTs displayed irregular thickened wall and less solid portion. MEOTs were more frequently characterized as solid or predominantly solid mass (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) than BeEOTs and BEOTs. The multivariate logistic regression analysis showed that volume of the solid portion (\u003cem\u003eP \u003c/em\u003e= 0.006) , maximum diameter of the solid portion(\u003cem\u003eP \u003c/em\u003e= 0.038), enhancement degrees (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001), and peritoneal carcinomatosis (\u003cem\u003eP \u003c/em\u003e= 0.011) were significant indicators for the differential diagnosis of the three groups. The area under the curves (AUCs) of above indicators and combination of four image features except peritoneal carcinomatosis for the differential diagnosis of BeEOTs and BEOTs, BEOTs and MEOTs ranged from 0.74 to 0.85, 0.58 to 0.79, respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: In this study, the characteristics of MRI can provide objective quantitative indicators for the accurate imaging diagnosis of three categories of EOTs and are helpful for clinical decision-making. Among these MRI characteristics, the volume, diameter, and enhancement degrees of the solid portion showed good diagnostic performance.\u003c/p\u003e","manuscriptTitle":"Quantitative Analysis of the MRI Features in the Differentiation of Benign, Borderline, and Malignant Epithelial Ovarian Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-22 14:35:58","doi":"10.21203/rs.3.rs-350729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-08-06T20:53:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-29T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2021-04-21T00:00:00+00:00","index":1,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-04-20T00:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-20T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-04-19T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-04-19T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2021-03-20T10:51:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"131bfa5e-15c0-4d56-b90b-0c47b0a4e447","owner":[],"postedDate":"April 22nd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":3824971,"name":"Sexual \u0026 Reproductive Medicine"},{"id":3824972,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2021-09-07T20:27:51+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-22 14:35:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-350729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-350729","identity":"rs-350729","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}