Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors

Ultrasound-based ADNEX model for differentiating between 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 Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors Wenting Xie, Qianyi Zhang, Yaoqin Wang, Zhisheng Xiang, Piaoyi Zeng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3893615/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The purpose of this study was to evaluate the ability of the International Ovarian Tumor Analysis-Assessment of Different NEoplasias in the adneXa (IOTA-ADNEX) model to distinguish between benign, borderline, and malignant epithelial ovarian tumors(BeEOTs, BEOTs, and MEOTs, respectively). Methods: The study included 813 patients with BeEOTs, BEOTs, and MEOTs who underwent ultrasound examinations and pelvic operations. Comparisons were made between the clinical information and ultrasonographic features of the three patient groups. Three clinical variables and six ultrasound variables were used to estimate malignancy risk. The sensitivity, specificity, positive predictive value, negative predictive value, and AUC (the area under the receiver operating characteristics [ROC] curve) of the ADNEX model were calculated. Results: Of the 813 patients, 257 (31.6%) had BeEOTs, 114 (14.0%) had BEOTs, and 442 (54.4%) had MEOTs. The most common type, serous and mucinous epithelial tumors, accounted for 81.3% of the total cases. In the MEOTs group, serous, mucinous, endometrioid, and clear cell tumors accounted for 85.3%, 2.5%, 4.5%, and 7.7% of the total cases, respectively. For a cut-off value of 10% to identify the overall risk for ovarian cancer (OC), the sensitivity and specificity were 99.1% and 73.2%, respectively. According to the ROC curves, the AUC was 0.987 (95% CI: 0.981–0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768–0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876–0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992–0.998) for BeEOTs compared with stages II–IV OC. The AUC was 0.614 (95% CI: 0.519–0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869–0.937) for BEOTs compared with stages II–IV OC, and 0.851 (95% CI: 0.800–0.902) for stage I OC compared with stages II–IV OC. Conclusions: The IOTA-ADNEX model demonstrated good diagnostic performance for the three categories of EOTs and can be helpful for clinical treatment management. Epithelial ovarian tumors Differential diagnosis IOTA ADNEX model Ultrasound Figures Figure 1 Figure 2 Figure 3 Introduction Adnexal masses are found in both pre- and postmenopausal women and occur throughout the life cycle[ 1 ]. Ovarian carcinoma is the most aggressive gynecological malignancy, with a five-year survival rate of less than 50%[ 2 ]. Epithelial ovarian tumors (EOTs) account for over 90% of all ovarian tumors and are responsible for most deaths[ 3 ]. EOTs are classified as benign, borderline, and malignant categories as per histological results[ 4 ]. Malignant epithelial ovarian tumors (MEOTs) have treatment strategies and survival rates different from benign and borderline epithelial ovarian tumors (BeEOTs and BEOTs, respectively). Patients with benign masses can be managed conservatively, and conservative surgery can be performed on women with BEOTs to preserve their fertility when considering their desire[ 5 ]. In contrast, the primary treatment of MEOTs is complete staging surgery combined with platinum-based chemotherapy[ 6 ]. Accordingly, preoperative prediction of the subtype of EOTs is critical for disease management and decision-making. Ultrasonography is a common imaging tool for visualizing adnexal masses, especially transvaginal ultrasound examination. Nevertheless, ultrasonography has certain limitations, such as the dependence of the result interpretation on the experience of sonographers[ 7 ]. The morphologic features of the masses are the basis for categorizing the risk of malignancy in ultrasound diagnosis[ 8 ]. Therefore, accurate description and correct interpretation of the ultrasound images is a key prerequisite for the diagnosis. Many prediction models have been developed to assist radiologists in improving diagnostic accuracy and reducing subjective differences[ 9 – 11 ]. IOTA (International Ovarian Tumor Analysis) group developed the ADNEX (Assessment of Different NEoplasias in the adneXa ) model for the diagnosis of the adnexal tumors[ 12 ]. It is the first multi-classification model that consists of three clinical indexes and six ultrasonic indexes[ 13 ]. A previous study demonstrated that the ADNEX model showed diagnostic performance for identifying adnexal lesions[ 14 , 15 ]. However, no studies have reported the validation of the ADNEX model in epithelial ovarian tumors. This present study aimed to evaluate the diagnostic ability of the ADNEX model in discriminating between BeEOTs, BEOTs, and MEOTs. Methods and Materials Study populations Patients diagnosed with EOTs from pelvic operations were retrospectively collected between December 2016 and January 2023. The inclusion criteria of this study were as follows: (a) patients underwent ultrasound examination before surgery, and clinicopathological data were complete; (b) patients were older than 14 years old; (c) no history of chemotherapy or gynecological operations. The exclusion criteria were as following: (a) postoperative pathological proven adnexal masses were not derived from ovarian tissue; (b) poor-quality images. A total of 813 patients with BeEOTs (n = 257), BEOTs (n = 114), or MEOTs (n = 442) were included in present study (Fig. 1 ). Patients who had bilateral lesions, the tumor with a more complicated ultrasound morphology or with large diameter were chosen for the analysis. Clinical characteristics data were retrieved retrospectively. Each patient’s age at diagnosis, location, menopausal status, and oncological marker level (i.e., CA125, AFP, HE4, and CEA) were recorded. All patients were examined with transabdominal or transvaginal ultrasound by one of the sonographers at our center. Sonographic characteristics of all patients were assessed according to the IOTA-ADNEX model. Ultrasound machines were GE Healthcare Ultrasound with transabdominal probes measuring 1–6 MHz and transvaginal probes measuring 2–9 MHz; Philips equipped with transabdominal probes measuring 1–5 MHz and transvaginal probes measuring 4–8 MHz; and Supersonic Aixplorer with transabdominal probes measuring 1–6 MHz and transvaginal probes measuring 3–12 MHz. Ultrasound-based ADNEX model The data of the ADNEX model was obtained from using mobile applications. The malignancy risk was estimated according to the three clinical variables as well as six ultrasound variables in the model[ 16 , 17 ]: age (years), type of center (oncology or non-oncology center), serum CA125 level (U/mL), maximal diameter of the lesion (mm), lesion diameter at its largest solid component (mm), cyst locules more than 10 (yes/no), number of papillary projections (0,1,2,3, or > 3), ascites (yes/no), and the presence of acoustic shadows (yes/no). The results were displayed in numerical forms and graphically to present the likelihood of different tumors after inputting the nine predictors. According to the IOTA-ADNEX model, EOTs were classified into four tumor types: BeEOT, BEOTs, stage I OC, and stages II–IV OC. Statistical analysis SPSS 20.0 software (IBM, Armonk, NY, USA) was used for statistical analysis. Categorical data were described as frequency and percentage. Continuous data were expressed as mean and standard deviation or median and interquartile range. The median test was used to compare the differences among the three types of tumors. Borderline tumor was considered malignant when discriminating between benign and malignant EOTs. The area under the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the ADNEX model to distinguish BeEOT from MEOT. Specificity, sensitivity, negative predictive values (NPV), and positive predictive values (PPV) were calculated to compare the diagnostics of the IOTA-ADNEX model at different levels of risk threshold (i.e., cut-off values of 10%, 20%, 30%, and 40% for the total risk of malignancy). In addition, the best cut-off point was calculated for differentiation between BeEOT and MEOTs. P-values of < 0.05 were regarded as statistically significant. Results Clinical and ultrasound characteristics of EOTs Table 1 shows the clinical and ultrasound features of the EOT patients. Total of 813 patients were enrolled in present study, of whom 257 (31.6%) had BeEOT, 114 (14.0%) had BEOT, and 442 (54.4%) had MEOT. Among MEOT patients, 48 patients had stage I, and 362 had stages II–IV, while 32 could not be pathologically staged. Median age of BeEOT, BEOT, stage I OC, and stages II–IV OC patients was 45 years (interquartile range [IQR], 36–53 years), 45 years (IQR, 35.5–55.0 years), 54 years (IQR, 47–58 years), and 55 years (IQR, 48–62), respectively. Women with malignant adnexal masses were older than patients with benign and borderline tumors. Among those patients, 444 (56.8%) were in the premenopausal stage, and 337 (43.1%) were in the postmenopausal stage. A higher proportion of postmenopausal women was more common in patients with malignant adnexal masses (53.1%, 278/524) than in the benign group (22.9%, 59/257). Tumor markers such as serum CA125, CA199, CEA, AFP, and HE4 exhibited significant differences between groups (p<0.05). Serum CA125 and HE4 values were increased in the stages II–IV OC group, i.e., 873.4 (336.7–2201.5) U/mL, p < 0.001, 322.2 (119.2-887.5) pmol/L, p < 0.001). Table 1 Clinical indicators and ultrasonic manifestations of epithelial ovarian tumors. Characteristic Benign (n = 257) Borderline (n = 114) Stage I (n = 48) Stage II-IV (n = 362) P Age at diagnosis (Median) 45(36–53) 45(35.5–55) 54(47–58) 55(48–62) < 0.001 Menopausal status(%) < 0.001 Premenopausal 198(77.1) 78(68.4) 22(45.8) 146(40.3) Postmenopausal 59(22.9) 36(31.6) 26(54.2) 216(59.7) Serum CA125 (U/ml) 20.2 (12.0-51.5) 75.0 (28.7-257.5) 113.5 (31.4-300.8) 873.4 (336.7-2201.5) < 0.001 Location(%) < 0.001 Left 135(52.5) 56(49.1) 22(45.8) 97(26.8) Right 111(43.2) 43(37.7) 22(45.8) 83(22.9) Bilateral 11(4.3) 15(13.2) 4(8.4) 182(50.3) CA199 (U/ml) 9.0(1.4–19.4) 18.4(6.2–91.9) 16.6(9.2–48.3) 9.2(4.0-20.8) < 0.001 CEA (ng/ml) 1.2(0.5–1.9) 1.4(0.8–2.4) 1.6(0.8–2.7) 1.1(0.6–1.9) 0.047 AFP (ng/ml) 1.8(0-2.8) 2.0(1.0–3.0) 2.3(1.4–3.7) 2.2(1.2–3.4) 0.003 HE4(pmol/L) 31.9 (22.3–41.0) 47.8 (33.2–86.5) 67.6 (40.3-141.5) 322.2 (119.2-887.5) < 0.001 Largest diameter of lesion (mm) 83.0 (58–117) 130.0 (85–207) 102.0 (69.5-145.5) 86.0 (55–117) < 0.001 Largest diameter of solid component(mm) 0 25.5 (0.0-64.3) 59.5 (39.3–99.5) 66.5 (47.0–97.0) < 0.001 Number of papillary projections(%) 3 3(1.2) 30(26.3) 14(29.2) 18(5.0) > 10 cyst locules(%) < 0.001 No 233(90.7) 71(62.3) 40(83.3) 336(92.8) Yes 24(9.3) 43(37.7) 8(16.7) 26(7.2) Ascites(%) < 0.001 No 247(96.1) 79(69.3) 38(79.2) 181(50) Yes 10(3.9) 35(30.7) 10(20.8) 181(50) Acoustic shadows < 0.001 No 257(100) 114(100) 48(100) 362(100) Yes 0 0 0 0 The ultrasonography features included in the ADNEX model were showed in Table 1 . The median tumor size was 83.0 (58–117) mm in the BeEOT group, 130.0 (85–207) mm in the BEOT group, 102.0 (69.5–145.5) mm in the stage I group, and 86.0 (55–117) mm in the stages II–IV group (p < 0.001). The largest diameter of borderline adnexal masses was greater than that of the other groups. According to these findings, the largest diameter of the solid component and ascites were observed significantly more frequently in stages II–IV group than that in the other groups (p 3 papillary projections were more common in borderline tumors than in benign and malignant EOTs. Adnexal masses with > 10 cyst locules were more commonly observed in BEOT than in other patients (p 10 cyst locules, the corresponding proportions for BeEOT, BEOT, stage I, and stages II–IV groups were 9.3%, 37.7%, 16.7%, and 7.2%, respectively. None of the EOT patients had acoustic shadows. Histological subtypes of EOTs The histological diagnosis and tumor-type distribution of EOTs are encapsulated in Table 2 . The most common type was serous and mucinous epithelial tumors, which accounted for 81.3% of all cases. In the MEOT group, serous, mucinous, endometrioid, and clear cell tumors accounted for 85.3%, 2.5%, 4.5%, and 7.7%, respectively. Table 2 Histological subtypes of epithelial ovarian tumors. Benign(n = 257) Borderline(n = 114) Malignant(n = 442) Serous tumours 99(38.5) 47(41.2) 377(85.3) Mucinous tumors 78(30.4) 49(43.0) 11(2.5) Endometrioid tumors 78(30.4) 7(6.1) 20(4.5) Clear cell tumors 0(0) 1(0.9) 34(7.7) Brenner tumours 2(0.7) 1(0.9) 0(0) Serous-mucinous tumors 0(0) 9(7.9) 0(0) The number in parenthesis is the percentage The IOTA-ADNEX model for assessing benign and malignant EOTs Figure 2 and Table 3 show the performance of the IOTA-ADNEX model for discriminating between MEOTs and BeEOTs at threshold risk values of malignancy of 10%, 20%, 30%, and 40%, and the best cut-off value. Using the best cut-off of 59.95% to predict malignancy, the model demonstrated excellent performance, with an AUC of 0.987 (95% CI: 0.981–0.993), a sensitivity was 92.8% (95% CI: 0.901–0.951), and a specificity of 96.5% (95% CI: 0.932–0.983) in BeEOT and MEOT groups. Table 3 Diagnostic performance of ADNEX model in discriminating between benign and malignant epithelial ovarian tumors. Threshold for probability of malignancy AUC (95% CI) Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) NPV (95% CI) 10% 0.861 (0.827, 0.859) 0.991 (0.975, 0.997) 0.732 (0.672, 0.784) 0.864 (0.830, 0.892) 0.979 (0.944, 0.993) 20% 0.899 (0.870, 0.928) 0.989 (0.972, 0.996) 0.809 (0.754,0.854) 0.899 (0.868, 0.923) 0.976 (0.943, 0.991) 30% 0.920 (0.894, 0.946) 0.973 (0.952, 0.985) 0.867 (0.818, 0.905) 0.927 (0.898, 0.948) 0.949 (0.910, 0.972) 40% 0.931 (0.907, 0.955) 0.959 (0.935, 0.975) 0.903 (0.858, 0.935) 0.944 (0.918, 0.963) 0.928 (0.887, 0.956) 59.95% 0.987 (0.981, 0.993) 0.928 (0.901, 0.951) 0.965 (0.932, 0.983) 0.978 (0.958, 0.989) 0.889 (0.845, 0.922) The ability of the IOTA- ADNEX model in different types of EOTs The discrimination performance of the IOTA-ADNEX model on different sub-classification tumors is presented in Fig. 3 and Table 4 . According to the ROC curves, the AUC was 0.987 (95% CI: 0.981–0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768–0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876–0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992–0.998) for BeEOT compared with stages II–IV OC. The AUC was 0.614 (95% CI: 0.519–0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869–0.937) for BEOTs compared with stage II–IV OC, and 0.851 (95% CI: 0.800–0.902) for stage I OC compared with stages II–IV OC. Table 4 Discrimination performance of ADNEX model on different type tumors. Discrimination AUC (95% CI) Benign vs malignant 0.987 (0.981, 0.993) Benign vs BEOTs 0.820 (0.768, 0.872) Benign vs Stage I OC 0.912 (0.876, 0.948) Benign vs Stage II-IV OC 0.995 (0.992, 0.998) BEOT vs Stage I OC 0.614 (0.519, 0.709) BEOT vs Stage II-IV OC 0.903 (0.869,0.937) Stage I OC vs Stage II-IV OC 0.851 (0.800, 0.902) Discussion To the best of our knowledge, few studies of the IOTA-ADNEX model in discriminating ovarian masses have been performed in a Chinese setting. Our study revealed that the IOTA-ADNEX model demonstrated excellent diagnostic performance for discriminating between BeEOTs and MEOTs, with CA125 included in the model (AUC was 0.98). This was similar to the finding previously reported by Poonyakanok et al., who estimated an AUC of 0.975[ 18 ]. The IOTA-ADNEX model also showcased a good performance in distinguishing between subtypes of epithelial tumors in our study (AUCs ranged from 0.61 to 0.99), which is in accordance with the findings of Chen et al.[ 19 ]. A previous study by Lei et al. indicated that the ADNEX model also demonstrated excellent performance in distinguishing between benign and malignant ovarian Brenner tumors[ 20 ]. Until now, there has been no recommended cut-off published. A cut-off value of 10% is the most commonly selected criterion to identify the overall risk for malignancy[ 19 ]. When using the ADNEX model to distinguish between benign and malignant tumors, different cut-off values were calculated to assess the diagnostic performance in our study. When using 10% as the cut-off value to identify the overall risk for OC, the sensitivity was 99.1%, and the specificity was 73.2%. Huang X et al. reported that the ADNEX model had high diagnostic accuracy for OC at the cut-off value of 15%[ 13 ]. In the present study, the best cut-off value was 59.95%. The model had a sensitivity of 98.7% (95% CI: 98.1–99.3%) and a specificity of 92.8% (95% CI: 90.1–95.1%). Peng et al. identified the optimal cut-off point to be 46.7% using the ADNEX model for detecting malignant ovarian tumors by the Youden index method[ 21 ]. However, the cut-off value for OC risk is flexible, and the cut-off selected is dependent on the type of centers and patients’clinical and pathological characteristics[ 13 ]. Borderline ovarian tumors account for 15–20% of all epithelial ovarian malignancies[ 22 ]. Our analysis revealed that the AUCs of the ADNEX model for the diagnosis of borderline versus benign, stage I, and stages II–IV OC were 0.820, 0.614, and 0.903, respectively. Borderline versus stage I OC had less diagnostic accuracy, which was similar to the findings reported by previous studies[ 21 , 23 ]. When discriminating between benign and stages II–IV malignant EOTs, we found that model was easy to use and had excellent performance with an AUC of 0.995, similar to a previous study by He et al.[ 8 ]. Ultrasound features are crucial for differentiating malignant tumors from all ovarian masses. Previous literature indicated that tumor diameter is related to malignancy[ 24 , 25 ]. Di Legge et al. found that adnexal masses with diameters smaller than 4 cm, between 4 and 10 cm, and > 10 cm had a probability of malignancy of 10%, 19%, and 40%, respectively[ 26 ]. In our study, we noted that borderline tumor had the largest diameter of lesion. Mar Pelayo et al. indicated that larger lesions have high probability of malignancy, but not all lesions with large diameters must be malignant[ 24 ]. Solid projections ≥ 3 mm in the cyst cavity were regarded as papillary projections[ 27 ]. Prior studies have suggested that papillary projections are related to borderline and malignant tumors[27, 28]. In this study, 20/257 (7.8%) benign tumors had papillary projections, which might have led to false-positive diagnoses of malignancy. None of the ovarian tumors in our study had acoustic shadows, which was associated with benignity[ 29 ]. This could be explained by the selection of the EOTs. Ascites was found in 3.9% (10/247) cases with benign adnexal masses and 43.1% (226/524) malignant ones. Stages II–IV OC had the highest probability among all groups. Our study had some limitations to consider. First, this was a retrospective study, which may have introduced selection bias. A prospective study is needed in future research. Second, this was single-center research, and the proportion of malignant tumors is the highest in our selected population. This may have introduced bias in the analysis of our results. Conclusions In summary, the IOTA-ADNEX model demonstrated good diagnostic performance for distinguishing between different epithelial ovarian tumors. This confirms that the ADNEX model is a reliable tool for assisting radiologists in the preoperative assessment of adnexal masses. Abbreviations EOTs Epithelial ovarian tumors MEOTs Malignant epithelial ovarian tumors BeEOTs benign epithelial ovarian tumors BEOTs borderline epithelial ovarian tumors IOTA International Ovarian Tumor Analysis ADNEX Assessment of Different NEoplasias in the adneXa CA125 Carbohydrate antigen125 AFP Alpha-fetoprotein HE4 Human Epididymis Protein 4 CEA Carcino-embryonic antigen ROC Receiver operating characteristics AUC Area under the receiver operating characteristics curve Declarations Ethics approval and consent to participate This study was approved by the institutional ethics committee of Fujian Cancer Hospital (NO. K2023-021-01). The requirement for informed patient consent for this retrospective study was waived. Consent for publication Not applicable Availability of data and materials All data generated or analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests. Funding This study was supported by Fujian Provincial Health Technology Project (NO.2022QNA044), the Fujian Provincial Natural Science Foundation of China (No. 2023J011240), Sciences Foundation of Fujian Cancer Hospital (NO.2023YN13). Authors' contributions WTX and QYZ contributed to the conceptualization. WTX was a major contributor in writing the manuscript. ZSX contributed to the software and analyzed the patient data. YQW, PYZ and ZSD performed the ultrasound examination of the adnexal masses. RH and LNT contributed to the writing—review and editing. All authors read and approved the final manuscript. Acknowledgements We thank all the patients who participated in this study. References Sisodia RC, Del Carmen MG: Lesions of the Ovary and Fallopian Tube . N Engl J Med 2022, 387 (8):727-736. Siegel RL, Miller KD, Fuchs HE, Jemal A: Cancer statistics, 2022 . CA Cancer J Clin 2022, 72 (1):7-33. Lheureux S, Gourley C, Vergote I, Oza AM: Epithelial ovarian cancer . 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Di Legge A, Testa AC, Ameye L, Van Calster B, Lissoni AA, Leone FP, Savelli L, Franchi D, Czekierdowski A, Trio D et al : Lesion size affects diagnostic performance of IOTA logistic regression models, IOTA simple rules and risk of malignancy index in discriminating between benign and malignant adnexal masses . Ultrasound Obstet Gynecol 2012, 40 (3):345-354. Timmerman D, Valentin L, Bourne TH, Collins WP, Verrelst H, Vergote I: 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. Moro F, Baima Poma C, Zannoni GF, Vidal Urbinati A, Pasciuto T, Ludovisi M, Moruzzi MC, Carinelli S, Franchi D, Scambia G et al : Imaging in gynecological disease (12): clinical and ultrasound features of invasive and non ‐invasive malignant serous ovarian tumors . Ultrasound in Obstetrics & Gynecology 2017, 50 (6):788-799. Hack K, Gandhi N, Bouchard-Fortier G, Chawla TP, Ferguson SE, Li S, Kahn D, Tyrrell PN, Glanc P: External Validation of O-RADS US Risk Stratification and Management System . Radiology 2022, 304 (1):114-120. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-3893615","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269452027,"identity":"f3889b36-4fe5-4e28-a232-dc66a216de84","order_by":0,"name":"Wenting Xie","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenting","middleName":"","lastName":"Xie","suffix":""},{"id":269452028,"identity":"2e94b562-0ce1-4c2a-9709-15007c26fa2e","order_by":1,"name":"Qianyi Zhang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianyi","middleName":"","lastName":"Zhang","suffix":""},{"id":269452029,"identity":"7f73e6c4-c221-46d2-b76f-c1b410274d56","order_by":2,"name":"Yaoqin Wang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaoqin","middleName":"","lastName":"Wang","suffix":""},{"id":269452030,"identity":"39cbd7cb-5fe3-4fa7-a61e-cbee99227eec","order_by":3,"name":"Zhisheng Xiang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhisheng","middleName":"","lastName":"Xiang","suffix":""},{"id":269452031,"identity":"ec2a54cf-072f-400b-b061-f900860a21c2","order_by":4,"name":"Piaoyi Zeng","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Piaoyi","middleName":"","lastName":"Zeng","suffix":""},{"id":269452032,"identity":"24857149-6f48-4343-8712-c7fa807e0a96","order_by":5,"name":"Ran Huo","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Huo","suffix":""},{"id":269452033,"identity":"498f2263-d976-4fed-893d-ce6caefdc4e1","order_by":6,"name":"Zhongshi Du","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhongshi","middleName":"","lastName":"Du","suffix":""},{"id":269452034,"identity":"d9152aa8-d773-4327-a054-7573c03a6624","order_by":7,"name":"Lina Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie2Qv2sCMRTHnwTuloNbz0X/AiHl4NrBev/KCwdOR+noeFDI6to/Qygc7RYJ3NTuDkItgjg4xK2Dg8/g4JKzo9B8IC8/yIeXbwA8nltlxQFiphB+Ktqx00nL7ehUkJSuRARhFXLwqkKDN1StAleUPPzSW3xe9tMG1ka8D/P7MJ4bhFFvUDm6RE/jB+Sbu7qh94nPsfh4YSxBKNJMuR5WZhy57tTfFaKQmtYMSFGidinxzir5mwRUpOSksN9WJSnTFSliFlB4UjozzYL2LotdRiF08dpAAULaLBmlc2cJp2VqzEE/TmVUmL2kH4vn64WZjHouhQiSc0O0E7+oLpg5N1R/uOzxeDz/kSNu9WBOrgqGXAAAAABJRU5ErkJggg==","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lina","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-01-24 09:29:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3893615/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3893615/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50389502,"identity":"2284198c-1869-4d49-8c7c-fc23921246ea","added_by":"auto","created_at":"2024-01-30 18:28:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":487305,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of this study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3893615/v1/2234296caab3927056fca3c1.jpg"},{"id":50389501,"identity":"038fea2e-c66b-47f3-8939-a2b600230d7e","added_by":"auto","created_at":"2024-01-30 18:28:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186599,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC of the IOTA-ADNEX model for discriminating between MEOTs and BeEOTs at different threshold risk values of malignancy.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3893615/v1/687ac88e1533194d5634c9af.jpg"},{"id":50389503,"identity":"235defb3-f0b4-48f5-b851-66c6327fe18c","added_by":"auto","created_at":"2024-01-30 18:28:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":643799,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC of the IOTA-ADNEX model for discriminating different sub-classification tumors.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3893615/v1/6eca7741ae08ef2fad327336.jpg"},{"id":68809068,"identity":"3478ad39-e020-4092-947f-f757488e6857","added_by":"auto","created_at":"2024-11-12 08:47:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2786586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3893615/v1/a13a9664-9a9d-47aa-8f46-46fc0825c1cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdnexal masses are found in both pre- and postmenopausal women and occur throughout the life cycle[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Ovarian carcinoma is the most aggressive gynecological malignancy, with a five-year survival rate of less than 50%[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Epithelial ovarian tumors (EOTs) account for over 90% of all ovarian tumors and are responsible for most deaths[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. EOTs are classified as benign, borderline, and malignant categories as per histological results[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Malignant epithelial ovarian tumors (MEOTs) have treatment strategies and survival rates different from benign and borderline epithelial ovarian tumors (BeEOTs and BEOTs, respectively). Patients with benign masses can be managed conservatively, and conservative surgery can be performed on women with BEOTs to preserve their fertility when considering their desire[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast, the primary treatment of MEOTs is complete staging surgery combined with platinum-based chemotherapy[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Accordingly, preoperative prediction of the subtype of EOTs is critical for disease management and decision-making.\u003c/p\u003e \u003cp\u003eUltrasonography is a common imaging tool for visualizing adnexal masses, especially transvaginal ultrasound examination. Nevertheless, ultrasonography has certain limitations, such as the dependence of the result interpretation on the experience of sonographers[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The morphologic features of the masses are the basis for categorizing the risk of malignancy in ultrasound diagnosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, accurate description and correct interpretation of the ultrasound images is a key prerequisite for the diagnosis. Many prediction models have been developed to assist radiologists in improving diagnostic accuracy and reducing subjective differences[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR36\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIOTA (International Ovarian Tumor Analysis) group developed the ADNEX (Assessment of Different NEoplasias in the adneXa ) model for the diagnosis of the adnexal tumors[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It is the first multi-classification model that consists of three clinical indexes and six ultrasonic indexes[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A previous study demonstrated that the ADNEX model showed diagnostic performance for identifying adnexal lesions[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, no studies have reported the validation of the ADNEX model in epithelial ovarian tumors.\u003c/p\u003e \u003cp\u003eThis present study aimed to evaluate the diagnostic ability of the ADNEX model in discriminating between BeEOTs, BEOTs, and MEOTs.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy populations\u003c/h2\u003e \u003cp\u003ePatients diagnosed with EOTs from pelvic operations were retrospectively collected between December 2016 and January 2023. The inclusion criteria of this study were as follows: (a) patients underwent ultrasound examination before surgery, and clinicopathological data were complete; (b) patients were older than 14 years old; (c) no history of chemotherapy or gynecological operations. The exclusion criteria were as following: (a) postoperative pathological proven adnexal masses were not derived from ovarian tissue; (b) poor-quality images. A total of 813 patients with BeEOTs (n\u0026thinsp;=\u0026thinsp;257), BEOTs (n\u0026thinsp;=\u0026thinsp;114), or MEOTs (n\u0026thinsp;=\u0026thinsp;442) were included in present study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients who had bilateral lesions, the tumor with a more complicated ultrasound morphology or with large diameter were chosen for the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinical characteristics data were retrieved retrospectively. Each patient\u0026rsquo;s age at diagnosis, location, menopausal status, and oncological marker level (i.e., CA125, AFP, HE4, and CEA) were recorded.\u003c/p\u003e \u003cp\u003eAll patients were examined with transabdominal or transvaginal ultrasound by one of the sonographers at our center. Sonographic characteristics of all patients were assessed according to the IOTA-ADNEX model. Ultrasound machines were GE Healthcare Ultrasound with transabdominal probes measuring 1\u0026ndash;6 MHz and transvaginal probes measuring 2\u0026ndash;9 MHz; Philips equipped with transabdominal probes measuring 1\u0026ndash;5 MHz and transvaginal probes measuring 4\u0026ndash;8 MHz; and Supersonic Aixplorer with transabdominal probes measuring 1\u0026ndash;6 MHz and transvaginal probes measuring 3\u0026ndash;12 MHz.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eUltrasound-based ADNEX model\u003c/h2\u003e \u003cp\u003eThe data of the ADNEX model was obtained from using mobile applications. The malignancy risk was estimated according to the three clinical variables as well as six ultrasound variables in the model[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e17\u003c/span\u003e]: age (years), type of center (oncology or non-oncology center), serum CA125 level (U/mL), maximal diameter of the lesion (mm), lesion diameter at its largest solid component (mm), cyst locules more than 10 (yes/no), number of papillary projections (0,1,2,3, or \u0026gt;\u0026thinsp;3), ascites (yes/no), and the presence of acoustic shadows (yes/no). The results were displayed in numerical forms and graphically to present the likelihood of different tumors after inputting the nine predictors. According to the IOTA-ADNEX model, EOTs were classified into four tumor types: BeEOT, BEOTs, stage I OC, and stages II\u0026ndash;IV OC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 20.0 software (IBM, Armonk, NY, USA) was used for statistical analysis. Categorical data were described as frequency and percentage. Continuous data were expressed as mean and standard deviation or median and interquartile range. The median test was used to compare the differences among the three types of tumors. Borderline tumor was considered malignant when discriminating between benign and malignant EOTs. The area under the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the ADNEX model to distinguish BeEOT from MEOT. Specificity, sensitivity, negative predictive values (NPV), and positive predictive values (PPV) were calculated to compare the diagnostics of the IOTA-ADNEX model at different levels of risk threshold (i.e., cut-off values of 10%, 20%, 30%, and 40% for the total risk of malignancy). In addition, the best cut-off point was calculated for differentiation between BeEOT and MEOTs. P-values of \u0026lt;\u0026thinsp;0.05 were regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClinical and ultrasound characteristics of EOTs\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the clinical and ultrasound features of the EOT patients. Total of 813 patients were enrolled in present study, of whom 257 (31.6%) had BeEOT, 114 (14.0%) had BEOT, and 442 (54.4%) had MEOT. Among MEOT patients, 48 patients had stage I, and 362 had stages II\u0026ndash;IV, while 32 could not be pathologically staged. Median age of BeEOT, BEOT, stage I OC, and stages II\u0026ndash;IV OC patients was 45 years (interquartile range [IQR], 36\u0026ndash;53 years), 45 years (IQR, 35.5\u0026ndash;55.0 years), 54 years (IQR, 47\u0026ndash;58 years), and 55 years (IQR, 48\u0026ndash;62), respectively. Women with malignant adnexal masses were older than patients with benign and borderline tumors. Among those patients, 444 (56.8%) were in the premenopausal stage, and 337 (43.1%) were in the postmenopausal stage. A higher proportion of postmenopausal women was more common in patients with malignant adnexal masses (53.1%, 278/524) than in the benign group (22.9%, 59/257). Tumor markers such as serum CA125, CA199, CEA, AFP, and HE4 exhibited significant differences between groups (p\u0026lt;0.05). Serum CA125 and HE4 values were increased in the stages II\u0026ndash;IV OC group, i.e., 873.4 (336.7\u0026ndash;2201.5) U/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 322.2 (119.2-887.5) pmol/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical indicators and ultrasonic manifestations of epithelial ovarian tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStage II-IV\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;362)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis (Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(36\u0026ndash;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(35.5\u0026ndash;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(47\u0026ndash;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55(48\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal status(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198(77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146(40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216(59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum CA125 (U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003cp\u003e(12.0-51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003cp\u003e(28.7-257.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.5\u003c/p\u003e \u003cp\u003e(31.4-300.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e873.4\u003c/p\u003e \u003cp\u003e(336.7-2201.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83(22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182(50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA199 (U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0(1.4\u0026ndash;19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4(6.2\u0026ndash;91.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.6(9.2\u0026ndash;48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2(4.0-20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2(0.5\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4(0.8\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6(0.8\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1(0.6\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8(0-2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0(1.0\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3(1.4\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2(1.2\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHE4(pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003cp\u003e(22.3\u0026ndash;41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003cp\u003e(33.2\u0026ndash;86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003cp\u003e(40.3-141.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e322.2\u003c/p\u003e \u003cp\u003e(119.2-887.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLargest diameter of lesion (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003cp\u003e(58\u0026ndash;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.0\u003c/p\u003e \u003cp\u003e(85\u0026ndash;207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.0\u003c/p\u003e \u003cp\u003e(69.5-145.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003cp\u003e(55\u0026ndash;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLargest diameter of solid component(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003cp\u003e(0.0-64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003cp\u003e(39.3\u0026ndash;99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003cp\u003e(47.0\u0026ndash;97.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of papillary projections(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237(92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336(92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 cyst locules(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233(90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336(92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247(96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcoustic shadows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e362(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ultrasonography features included in the ADNEX model were showed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median tumor size was 83.0 (58\u0026ndash;117) mm in the BeEOT group, 130.0 (85\u0026ndash;207) mm in the BEOT group, 102.0 (69.5\u0026ndash;145.5) mm in the stage I group, and 86.0 (55\u0026ndash;117) mm in the stages II\u0026ndash;IV group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The largest diameter of borderline adnexal masses was greater than that of the other groups. According to these findings, the largest diameter of the solid component and ascites were observed significantly more frequently in stages II\u0026ndash;IV group than that in the other groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). None of those in the BeEOT group had a solid component, while the largest diameter of the solid component was 66.5 (47.0\u0026ndash;97.0) mm in the stages II\u0026ndash;IV group. Adnexal lesions with \u0026gt;\u0026thinsp;3 papillary projections were more common in borderline tumors than in benign and malignant EOTs. Adnexal masses with \u0026gt;\u0026thinsp;10 cyst locules were more commonly observed in BEOT than in other patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding the feature of \u0026gt;\u0026thinsp;10 cyst locules, the corresponding proportions for BeEOT, BEOT, stage I, and stages II\u0026ndash;IV groups were 9.3%, 37.7%, 16.7%, and 7.2%, respectively. None of the EOT patients had acoustic shadows.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHistological subtypes of EOTs\u003c/h2\u003e \u003cp\u003eThe histological diagnosis and tumor-type distribution of EOTs are encapsulated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The most common type was serous and mucinous epithelial tumors, which accounted for 81.3% of all cases. In the MEOT group, serous, mucinous, endometrioid, and clear cell tumors accounted for 85.3%, 2.5%, 4.5%, and 7.7%, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHistological subtypes of epithelial ovarian tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign(n\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBorderline(n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalignant(n\u0026thinsp;=\u0026thinsp;442)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous tumours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377(85.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear cell tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrenner tumours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous-mucinous tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe number in parenthesis is the percentage\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe IOTA-ADNEX model for assessing benign and malignant EOTs\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the performance of the IOTA-ADNEX model for discriminating between MEOTs and BeEOTs at threshold risk values of malignancy of 10%, 20%, 30%, and 40%, and the best cut-off value. Using the best cut-off of 59.95% to predict malignancy, the model demonstrated excellent performance, with an AUC of 0.987 (95% CI: 0.981\u0026ndash;0.993), a sensitivity was 92.8% (95% CI: 0.901\u0026ndash;0.951), and a specificity of 96.5% (95% CI: 0.932\u0026ndash;0.983) in BeEOT and MEOT groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of ADNEX model in discriminating between benign and malignant epithelial ovarian tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003cp\u003efor probability of malignancy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003cp\u003e(0.827, 0.859)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003cp\u003e(0.975, 0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003cp\u003e(0.672, 0.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003cp\u003e(0.830, 0.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003cp\u003e(0.944, 0.993)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003cp\u003e(0.870, 0.928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003cp\u003e(0.972, 0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003cp\u003e(0.754,0.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003cp\u003e(0.868, 0.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003cp\u003e(0.943, 0.991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003cp\u003e(0.894, 0.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003cp\u003e(0.952, 0.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003cp\u003e(0.818, 0.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003cp\u003e(0.898, 0.948)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003cp\u003e(0.910, 0.972)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003cp\u003e(0.907, 0.955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003cp\u003e(0.935, 0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003cp\u003e(0.858, 0.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003cp\u003e(0.918, 0.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003cp\u003e(0.887, 0.956)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e59.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003cp\u003e(0.981, 0.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003cp\u003e(0.901, 0.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003cp\u003e(0.932, 0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003cp\u003e(0.958, 0.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003cp\u003e(0.845, 0.922)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe ability of the IOTA- ADNEX model in different types of EOTs\u003c/h2\u003e \u003cp\u003eThe discrimination performance of the IOTA-ADNEX model on different sub-classification tumors is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. According to the ROC curves, the AUC was 0.987 (95% CI: 0.981\u0026ndash;0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768\u0026ndash;0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876\u0026ndash;0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992\u0026ndash;0.998) for BeEOT compared with stages II\u0026ndash;IV OC. The AUC was 0.614 (95% CI: 0.519\u0026ndash;0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869\u0026ndash;0.937) for BEOTs compared with stage II\u0026ndash;IV OC, and 0.851 (95% CI: 0.800\u0026ndash;0.902) for stage I OC compared with stages II\u0026ndash;IV OC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscrimination performance of ADNEX model on different type tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscrimination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign vs malignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.987 (0.981, 0.993)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign vs BEOTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.820 (0.768, 0.872)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign vs Stage I OC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.912 (0.876, 0.948)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign vs Stage II-IV OC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.995 (0.992, 0.998)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBEOT vs Stage I OC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.614 (0.519, 0.709)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBEOT vs Stage II-IV OC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.903 (0.869,0.937)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I OC vs Stage II-IV OC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.851 (0.800, 0.902)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, few studies of the IOTA-ADNEX model in discriminating ovarian masses have been performed in a Chinese setting. Our study revealed that the IOTA-ADNEX model demonstrated excellent diagnostic performance for discriminating between BeEOTs and MEOTs, with CA125 included in the model (AUC was 0.98). This was similar to the finding previously reported by Poonyakanok et al., who estimated an AUC of 0.975[\u003cspan\u003e18\u003c/span\u003e]. The IOTA-ADNEX model also showcased a good performance in distinguishing between subtypes of epithelial tumors in our study (AUCs ranged from 0.61 to 0.99), which is in accordance with the findings of Chen et al.[\u003cspan\u003e19\u003c/span\u003e]. A previous study by Lei et al. indicated that the ADNEX model also demonstrated excellent performance in distinguishing between benign and malignant ovarian Brenner tumors[\u003cspan\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eUntil now, there has been no recommended cut-off published. A cut-off value of 10% is the most commonly selected criterion to identify the overall risk for malignancy[\u003cspan\u003e19\u003c/span\u003e]. When using the ADNEX model to distinguish between benign and malignant tumors, different cut-off values were calculated to assess the diagnostic performance in our study. When using 10% as the cut-off value to identify the overall risk for OC, the sensitivity was 99.1%, and the specificity was 73.2%. Huang X et al. reported that the ADNEX model had high diagnostic accuracy for OC at the cut-off value of 15%[\u003cspan\u003e13\u003c/span\u003e]. In the present study, the best cut-off value was 59.95%. The model had a sensitivity of 98.7% (95% CI: 98.1–99.3%) and a specificity of 92.8% (95% CI: 90.1–95.1%). Peng et al. identified the optimal cut-off point to be 46.7% using the ADNEX model for detecting malignant ovarian tumors by the Youden index method[\u003cspan\u003e21\u003c/span\u003e]. However, the cut-off value for OC risk is flexible, and the cut-off selected is dependent on the type of centers and patients’clinical and pathological characteristics[\u003cspan\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBorderline ovarian tumors account for 15–20% of all epithelial ovarian malignancies[\u003cspan\u003e22\u003c/span\u003e]. Our analysis revealed that the AUCs of the ADNEX model for the diagnosis of borderline versus benign, stage I, and stages II–IV OC were 0.820, 0.614, and 0.903, respectively. Borderline versus stage I OC had less diagnostic accuracy, which was similar to the findings reported by previous studies[\u003cspan\u003e21\u003c/span\u003e, \u003cspan\u003e23\u003c/span\u003e]. When discriminating between benign and stages II–IV malignant EOTs, we found that model was easy to use and had excellent performance with an AUC of 0.995, similar to a previous study by He et al.[\u003cspan\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eUltrasound features are crucial for differentiating malignant tumors from all ovarian masses. Previous literature indicated that tumor diameter is related to malignancy[\u003cspan\u003e24\u003c/span\u003e, \u003cspan\u003e25\u003c/span\u003e]. Di Legge et al. found that adnexal masses with diameters smaller than 4 cm, between 4 and 10 cm, and \u0026gt; 10 cm had a probability of malignancy of 10%, 19%, and 40%, respectively[\u003cspan\u003e26\u003c/span\u003e]. In our study, we noted that borderline tumor had the largest diameter of lesion. Mar Pelayo et al. indicated that larger lesions have high probability of malignancy, but not all lesions with large diameters must be malignant[\u003cspan\u003e24\u003c/span\u003e]. Solid projections ≥ 3 mm in the cyst cavity were regarded as papillary projections[\u003cspan\u003e27\u003c/span\u003e]. Prior studies have suggested that papillary projections are related to borderline and malignant tumors[27, 28]. In this study, 20/257 (7.8%) benign tumors had papillary projections, which might have led to false-positive diagnoses of malignancy. None of the ovarian tumors in our study had acoustic shadows, which was associated with benignity[\u003cspan\u003e29\u003c/span\u003e]. This could be explained by the selection of the EOTs. Ascites was found in 3.9% (10/247) cases with benign adnexal masses and 43.1% (226/524) malignant ones. Stages II–IV OC had the highest probability among all groups.\u003c/p\u003e\n\u003cp\u003eOur study had some limitations to consider. First, this was a retrospective study, which may have introduced selection bias. A prospective study is needed in future research. Second, this was single-center research, and the proportion of malignant tumors is the highest in our selected population. This may have introduced bias in the analysis of our results.\u003c/p\u003e\n\n"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the IOTA-ADNEX model demonstrated good diagnostic performance for distinguishing between different epithelial ovarian tumors. This confirms that the ADNEX model is a reliable tool for assisting radiologists in the preoperative assessment of adnexal masses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEOTs \u0026nbsp;Epithelial ovarian tumors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMEOTs \u0026nbsp;Malignant epithelial ovarian tumors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeEOTs \u0026nbsp;benign epithelial ovarian tumors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBEOTs \u0026nbsp;borderline epithelial ovarian tumors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIOTA \u0026nbsp;International Ovarian Tumor Analysis\u003c/p\u003e\n\u003cp\u003eADNEX \u0026nbsp;Assessment of Different NEoplasias in the adneXa\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCA125 \u0026nbsp;Carbohydrate antigen125\u003c/p\u003e\n\u003cp\u003eAFP \u0026nbsp;Alpha-fetoprotein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHE4 \u0026nbsp;Human Epididymis Protein 4\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCEA \u0026nbsp;Carcino-embryonic antigen\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp;Receiver operating characteristics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp;Area under the receiver operating characteristics curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional ethics committee of Fujian Cancer Hospital (NO. K2023-021-01). The requirement for informed patient consent for this retrospective study was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and 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 declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Fujian Provincial Health Technology Project (NO.2022QNA044), the Fujian Provincial Natural Science Foundation of China (No. 2023J011240), Sciences Foundation of Fujian Cancer Hospital (NO.2023YN13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWTX and QYZ contributed to the conceptualization. WTX was a major contributor in writing the manuscript. ZSX contributed to the software and analyzed the patient data. YQW, PYZ and ZSD performed the ultrasound examination of the adnexal masses. RH and LNT contributed to the writing\u0026mdash;review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the patients who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSisodia RC, Del Carmen MG: \u003cstrong\u003eLesions of the Ovary and Fallopian Tube\u003c/strong\u003e. \u003cem\u003eN Engl J Med \u003c/em\u003e2022, \u003cstrong\u003e387\u003c/strong\u003e(8):727-736.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A: \u003cstrong\u003eCancer statistics, 2022\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2022, \u003cstrong\u003e72\u003c/strong\u003e(1):7-33.\u003c/li\u003e\n\u003cli\u003eLheureux S, Gourley C, Vergote I, Oza AM: \u003cstrong\u003eEpithelial ovarian cancer\u003c/strong\u003e. \u003cem\u003eLancet (London, England) \u003c/em\u003e2019, \u003cstrong\u003e393\u003c/strong\u003e(10177):1240-1253.\u003c/li\u003e\n\u003cli\u003eWei M, Zhang Y, Bai G, Ding C, Xu H, Dai Y, Chen S, Wang H: \u003cstrong\u003eT2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study\u003c/strong\u003e. \u003cem\u003eInsights into Imaging \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eTomao F, Peccatori F, Del Pup L, Franchi D, Zanagnolo V, Panici PB, Colombo N: \u003cstrong\u003eSpecial issues in fertility preservation for gynecologic malignancies\u003c/strong\u003e. \u003cem\u003eCritical reviews in oncology/hematology \u003c/em\u003e2016, \u003cstrong\u003e97\u003c/strong\u003e:206-219.\u003c/li\u003e\n\u003cli\u003eNie S, Zhang L, Liu J, Wan Y, Jiang Y, Yang J, Sun R, Ma X, Sun G, Meng H\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eALKBH5-HOXA10 loop-mediated JAK2 m6A demethylation and cisplatin resistance in epithelial ovarian cancer\u003c/strong\u003e. \u003cem\u003eJournal of Experimental \u0026amp; 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Comparisons were made between the clinical information and ultrasonographic features of the three patient groups. Three clinical variables and six ultrasound variables were used to estimate malignancy risk. The sensitivity, specificity, positive predictive value, negative predictive value, and AUC (the area under the receiver operating characteristics [ROC] curve) of the ADNEX model were calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOf the 813 patients, 257 (31.6%) had BeEOTs, 114 (14.0%) had BEOTs, and 442 (54.4%) had MEOTs. The most common type, serous and mucinous epithelial tumors, accounted for 81.3% of the total cases. In the MEOTs group, serous, mucinous, endometrioid, and clear cell tumors accounted for 85.3%, 2.5%, 4.5%, and 7.7% of the total cases, respectively. For a cut-off value of 10% to identify the overall risk for ovarian cancer (OC), the sensitivity and specificity were 99.1% and 73.2%, respectively. According to the ROC curves, the AUC was 0.987 (95% CI: 0.981–0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768–0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876–0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992–0.998) for BeEOTs compared with stages II–IV OC. The AUC was 0.614 (95% CI: 0.519–0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869–0.937) for BEOTs compared with stages II–IV OC, and 0.851 (95% CI: 0.800–0.902) for stage I OC compared with stages II–IV OC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe IOTA-ADNEX model demonstrated good diagnostic performance for the three categories of EOTs and can be helpful for clinical treatment management.\u003c/p\u003e","manuscriptTitle":"Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 18:28:00","doi":"10.21203/rs.3.rs-3893615/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bfe7a738-a220-46ee-810f-7148e474b5eb","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-12T08:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 18:28:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3893615","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3893615","identity":"rs-3893615","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}