Estimating the Risk of Malignancy in Adnexal Masses: Validation of the ADNEX Model in the Hands of Non-expert Ultrasonographers in a Gynecological Oncology Center in China

Estimating the Risk of Malignancy in Adnexal Masses: Validation of the ADNEX Model in the Hands of Non-expert Ultrasonographers in a Gynecological Oncology Center in China | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Estimating the Risk of Malignancy in Adnexal Masses: Validation of the ADNEX Model in the Hands of Non-expert Ultrasonographers in a Gynecological Oncology Center in China Ping He, Jingjing Wang, Wei Duan, Chao Song, Yu Yang, Qingqing Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-630532/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: The diagnosis of adnexal masses depends more on ultrasonography. This study aim to validate the diagnostic accuracy of the International Ovarian Tumor Analysis (IOTA) ADNEX model in the preoperative diagnosis of adnexal masses in the hands of non-expert ultrasonographers in a gynecological oncology center in China. Methods: This was a single oncology center, retrospective diagnostic accuracy study from 620 patients. All patients underwent surgery and the histopathological diagnosis was used as reference standard. The masses were divided into five types according to the ADNEX model: benign ovarian tumor, borderline ovarian tumor (BOT), Stage-I ovarian cancer (OC), Stages-II-IV OC and ovarian metastasis. Receiver-operating characteristics (ROC) curve analysis was used to evaluate the ability of the ADNEX model to classify tumors into different histological types with and without Cancer antigen 125 (CA 125) results. Results: Of the 620 women, 402 (64.8%) had a benign ovarian tumor and, 218 (35.2%) had a malignant ovarian tumor, including 86 (13.9%) with BOT, 75 (12.1%) with Stage-I OC, 53 (8.5%) with Stages-II-IV OC and 4 (0.6%) with ovarian metastasis. The AUC of the model to differentiate between benign and malignant adnexal masses was 0.97 (95% CI, 0.96–0.98). Performance was excellent for the discrimination between benign vs Stage II-IV OC, benign vs ovarian metastasis with AUCs of 0.99 (95% CI, 0.99-1.00) and 0.99 (95% CI, 0.98-1.00), respectively. Performance of the model was less effective at distinguishing between BOT and Stage I OC and between BOT and ovarian metastasis, with AUC of 0.54 (95% CI, 0.45–0.64) and 0.66 (95% CI, 0.56–0.77), respectively. When including CA125 in the model, performance in discriminating between Stages II–IV OC with stage I OC and ovarian metastasis were improved (AUC increased from 0.88 to 0.94, P = 0.01; 0.86 to 0.97, p = 0.01, respectively). Conclusions: The IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of non-expert ultrasonographers with limited experienced in China. Between classification different subtypes of ovarian cancers, the model has difficulty to differentiate BOT from stage I OC, BOT from ovarian metastases. Obstetrics & Gynecology Oncology ADNEX model CA 125 diagnosis ovarian tumor ultrasonography Figures Figure 1 Figure 2 Introduction In Chinese women, the mortality rate of the three kinds of cancer is increasing year by year, including breast cancer, cervical cancer and ovarian cancer 1 , In particular, most of the patients are asymptomatic in the early stage of ovarian cancer. The five year survival rate of patients with stage III-IV ovarian cancer is less than 30%, that of patients with stage II is about 70%, and that of patients with stage I is more than 90% 2 . The combination of early diagnosis and timely treatment is considered to be the key factor to optimize the survival rate 3 , 4 . We diagnose ovarian cancer as a benign tumor incorrectly may delay the timing of treatment and lead to inadequate treatment, on the contrary, it will make patient undergo more extensive treatment and increase the possibility of postoperative complications. It is very essential to make a correct diagnosis. The diagnosis of adnexal masses depends more on ultrasonography. Some studies have reported that subjective evaluation of a tumor by an expert ultrasonographer is an excellent method for discriminating between benign and malignant adnexal masses 5 – 7 . It is necessary for doctors who are not so experienced to use a more objective method to assist in diagnosis. In order to characterize the ovarian tumors as benign or malignant, biomarkers combined with ultrasonography have been used to optimize the accuracy of diagnosis, including the risk of malignancy index (RMI). The International Ovarian Tumor Analysis group (IOTA) have presented a consensus on the terms, definitions and measurements used to describe the sonographic features of adnexal tumors 8 and standardized the description of ovarian lesions. Then IOTA developed and validated many models to discriminate between benign and malignant adnexal masses such as logistic regression model LR1, LR2, Simple Rules and so on 9 , 10 . In a meta-analysis 11 , the ability of different methods to differentiate benign from malignant adnexal masses was compared. The results showed that IOTA Simple Rules and LR2 were superior to RMI and to all other methods included in the meta-analysis. The Assessment of Different NEoplasias in the adneXa (ADNEX) model is the first predictive multiclass model developed by IOTA and is able to differentiate between benign tumors, borderline tumors (BOTs), stage-I ovarian cancer (OC), stage II-IV OC and secondary metastatic ovarian cancers 12 . Preoperative characterization of an adnexal mass is of crucial importance for selecting the optimal management strategy and differential diagnosis of the mass by the ADNEX model may help to optimize management. In recent years, several studies have been reported the model has good to excellent performance in their populations 13 – 15 . Also in China, it has been reported had high accuracy in distinguishing between benign and malignant adnexal masses by expert ultrasonographers in a gynecological oncology center in Shanghai 16 . However, there are few studies validating the discriminative performance of the ADNEX model in the hands of non-expert ultrasonographers, it has great hope as a method for the correct classification of adnexal masses by ultrasonographers with limited experience. The aim of our study was to evaluate the performance of the IOTA ADNEX model in the preoperative discrimination between benign, borderline, early and advanced stage invasive, and secondary metastatic tumors in the hands of non-expert ultrasonographers in a single oncology center in Beijing, China. Methods Study design and patients This was a single center retrospective study for diagnostic accuracy conducted at a tertiary referral oncology hospital. From 1 January 2018 to 31 December 2019, seven hundred and sixty-eight patients with an ultrasound diagnosis of an adnexal mass were recruited from the Department of Ultrasound in Beijing Obstetrics and Gynecology Hospital in China consecutively. The inclusion criteria were as follows: (1)patients presenting with at least one adnexal mass who underwent transvaginal or transrectal ultrasonography (supplemented with transabdominal if transvaginal is not sufficient); (2) the interval between operation and ultrasonography should not exceed 120 days. (3) The patient had no previous history of ovarian cancer. The exclusion criteria were as follows: (1) Cysts that were deemed to be clearly physiological and less than 3 cm in maximum diameter; (2) Previous bilateral adnexectomy. For bilateral adnexal masses, the mass with the most complex ultrasound features was included. If both masses had similar ultrasound morphology, the largest mass or the one most easily accessible by ultrasonography was included 17 . The study was approved by the Institutional Ethics Committee of Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University. Two non-expert ultrasonographers at level 2 according to the EFSUMB classification who have successfully passed the IOTA certification test exam, assessed the sonographic tumor morphology based on the standardized manner previously published by the IOTA group 8 . All assessments were done prior to obtaining pathology result, and the ultrasonographers were blinded to this outcome. The ultrasound machines used were Voluson E8 (GE Healthcare, USA) with 5.0–9.0 MHz transvaginal probes and 1.0–5.0 MHz transabdominal probes. Clinical and ultrasound variables of the ADNEX model were recorded. Serum CA125(U/ml) levels were assessed 7 days before surgery using an Elecsys and Cobas E analyzers (Roche, Mannheim, Germany). Reference standard The histopathological diagnosis of the mass after surgical removal by laparoscopy or laparotomy was used as reference standard. Tumors were staged according to the World Health Organization (WHO) classification of tumors and malignant tumors are staged using the International Federation of Obstetrics and Gynecology (FIGO) standards 18 . In the final diagnosis, the masses were divided into five types: benign, BOTs, stage I OC, stage II-IV OC, secondary metastatic cancer. Adnex Model We input the variables needed by the ADNEX model into the web application (( http://www.iotagroup.org/adnexmodel/ ). The model includes nine variables in the: age (years), serum CA125 level (U/mL), type of center (oncology referral center vs non-oncology center), maximal diameter of the lesion (mm), maximal diameter of the largest solid part (mm), number of papillary projections (0, 1, 2, 3 or more than 3), number of cyst locules (≤ 10 vs > 10), acoustic shadows (yes or no), and ascites (yes or no) 12 . All ADNEX model parameters were logged objectively. Then the model can calculate the patient specific risk and relative risk of each subtype. With or without CA125 result, the model is able to calculate the malignant risk. This study compared the diagnostic accuracy of the model with or without CA125 result. Statistical analysis We analyzed data using R software. For statistical purposes, BOTs were considered malignant. We compared clinical and sonographic features of adnexal masses of the ADNEX model using the chi-square test and Fisher’s exact test for categorical data and the Mann–Whitney U-test for continuous data. In order to validate the ADNEX model with and without CA125 level, receiver–operating characteristics (ROC) curve analysis was performed. We calculated the area under the curve (AUC) with 95% CIs for basic discrimination between benign and malignant adnexal tumors using the total risk of malignancy (i.e., the sum of the estimated risks of the four malignant subtypes). AUCs of ADNEX model with and without CA125 level were computed for each pair of tumor types using the DeLong’s test. We calculated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR-) at progressive cut-off points for total risk of malignancy and at the cut-off point determined by ROC curve analyses of our data. Statistical calculations were performed using 95% CIs, with P < 0.05 considered to be significant. Results Between 1 January 2018 to 31 December 2019, 768 patients with adnexal tumors were examined by ultrasonography before laparoscopy or laparotomy. 148women were excluded from the study because of pregnancy, failure to undergo surgery, incomplete clinical data, histological diagnosis of uterine lesion, diagnosis of an extragynecological tumor. Therefore, the final cohort consisted of 620 patients (Fig. 1 ). Among them, 402 (64.8%) had a benign tumor, 218 (35.2%) had a malignant tumor, including 86 (13.9%) with BOT, 75 (12.1%) with stage I OC, 53 (8.5%) with stage II-IV OC, and 4 (0.6%) with ovarian metastases. The most common benign tumors are serous cystadenoma and teratoma, while the most common malignant tumors are serous adenocarcinoma and clear cell carcinoma. Clinical and sonographic features of adnexal masses in our cohort are shown in Table 2 . The patients in the malignant group were older and had higher CA125 levels than those in the benign group. Prevalence of solid tissue, papillary projections and ascites are more common in the malignant group. Acoustic shadows are more common in benign tumor group. Besides these, prevalence of the features including the maximum diameter of the lesion and the largest solid component, more than 10 locules and presence of ascites were significant differences between benign and malignant masses. (p<0.05) Table 1 Histopathological findings in 620 women with adnexal mass Histological type n (%) Benign 402 (64.8) Serous cystadenoma 115 (18.5) Teratoma 111 (17.9) Mucinous cystadenoma 81 (13.1) Endometrioma 55 (8.9) Fibrothecoma 15 (2.4) Fibroma 7 (1.1) Adenofibroma 2 (0.3) Cystadenofibroma 2 (0.3) Paraovarian cyst 6 (1.0) Mesosalpinx cyst 4 (0.6) Other ovarian benign lesion 4 (0.6) Borderline 86 (13.9) Serous 33 (5.3) Mucinous 37 (6.0) Endometrioid 2 (0.3) Clear-cell 1 (0.2) sex cord-stromal tumors 13 (2.1) Primary Malignant 128 (20.6) Serous adenocarcinoma 40 (6.5) Clear cell carcinoma 31 (4.8) Mucinous adenocarcinoma 28 (4.5) Endometrioid adenocarcinoma 13 (2.1) Serous/mucinous adenocarcinoma 7 (1.1) Carcinosarcoma 3 (0.5) Immature teratoma 3 (0.5) Granulosa-cell tumor 2 (0.3) Sertoli leydig 1 (0.2) Ovarian metastasis 4 (0.6) Table 2 Sonographic features of tumor in 620 women with adnexal mass Malignant (n = 218) Benign Borderline Stage I OC Stages-II–IV OC metastasis total Characteristic (n = 402) (n = 86) (n = 75) (n = 53) (n = 4) (n = 620) p Age (years) 31 (27–39) 38 (30–48) 47 (41–53) 48 (44–57) 57 (46–62) 44 (34–52) < 0.001* CA 125 (U/mL) 11.4 (8–17) 18 (10–28) 37 (15–83) 204 (53–547) 66 (28–137) 26 (13–74) < 0.001 * Max diameter of lesion (mm) 63 (50–83) 88 (53–121) 106 (71–148) 88 (64–143) 81 (63–108) 92 (64–133) < 0.001* Presence of solid tissue 44(10.9) 63(73.3) 70(93.3) 53(100) 4(100) 190(30.6) < 0.001 † Maximum diameter of largest solid component, if present (mm) 30 (13–48) 31 (21–49) 46 (31–80) 66 (57–79) 74 (48–107) 45 (26–67) p = 0.001* Papillary projections present 15(3.7) 45(52.3) 36(48.0) 28(52.8) 0(0) 109(17.6) 3 1(0.2) 4(4.7) 6(8.0) 11(20.8) 0(0) 21(3.4) > 10 cyst locules 3(0.7) 15(17.4) 8(10.7) 4(7.5) 0(0) 27(4.4) < 0.001 † Acoustic shadows 121(30.1) 1(1.2) 5(6.7) 2(3.8) 0(0) 8(1.3) < 0.001 † Ascites 3(0.7) 4(4.7) 11(14.7) 30(56.6) 3(75.0) 48(7.7) < 0.001 † Data are given as median (interquartile range) or n (%). P for benign vs malignant groups calculated using: *Mann–Whitney U-test, †chi-square test or ‡Fisher’s exact test. OC, ovarian cancer. Validation Of Iota Adnex Model The diagnostic performance of the IOTA ADNEX model is presented in Fig. 2 . The AUC of the model to differentiate between benign and malignant adnexal masses was 0.97 (95% CI, 0.96–0.98). The performances of the IOTA ADNEX model with CA125 level at progressive cut-off points for probability of malignancy are shown in Table 3 . Sensitivity was 87.06% (82.09–93.03) and specificity was 97.69% (91.03–99.23) at an optimal cut-off of 39.2% probability of malignancy. Table 3 Performance of the ADNEX model in discriminating between benign and malignant tumors at progressive cut-offs for probability of malignancy Cut-off AUC (95% CI) Sensitivity (95% CI) (%) Specificity (95% CI) (%) PPV (95% CI) (%) NPV (95% CI) (%) LR+ (95% CI) LR- (95% CI) DOR 3% - 97.51 (95.02–99.50) 69.49 (64.87–74.10) 62.22 (58.70-66.01) 98.22 (96.55–99.63) 3.20 (2.75–3.72) 0.04 (0.02–0.09) 80.00 5% - 92.04 (88.05–95.52) 87.95 (84.62–91.03) 79.83 (75.30-84.16) 95.54 (93.33–97.49) 7.64 (5.82–10.02) 0.09 (0.06–0.15) 84.89 10% - 88.06 (83.58–92.54) 94.10 (91.79–96.41) 88.61 (84.54–92.57) 93.92 (91.71–96.05) 14.93 (10.01–22.26) 0.13 (0.09–0.18) 114.85 15% - 87.56 (82.59–92.04) 95.90 (93.85–97.69) 91.75 (88.02–95.31) 93.75 (91.57–95.84) 21.36 (13.18–34.61) 0.13 (0.09–0.19) 164.31 39.2% * 0.97 (0.96–0.98) 87.06 (82.09–93.03) 97.69 (91.03–99.23) 95.03 (84.07–98.35) 93.66 (91.41–96.24) 37.69 (18.09–78.53) 0.13 (0.09–0.19) 289.92 * Optimal cut-off, the maximum value of Youden index; AUC, area under receiver–operating characteristics curve; DOR, diagnostic odds ratio; LR+, positive likelihood ratio; LR–, negative likelihood ratio; NPV, negative predictive value; PPV, positive predictive value When tumors were classified into benign, BOTs, stage I OC, stage II-IV OC, secondary metastatic cancer, the model showed poor to excellent discrimination ability between the different subtypes, with AUCs varying between 0.54 and 0.99 when CA125 level was included in the model and between 0.50 and 0.99 without CA125 level (Table 4 ). And AUCs of the model in differentiating benign tumor from subtypes of malignant tumor are high. The AUC was 0.94 for benign tumors compared with borderline tumors, 0.98 for benign tumors compared with stage I OC, 0.99 for benign tumors compared with stage II-IV OC, and 0.99 for benign tumors compared with secondary metastatic cancer. The ability to discriminate between benign and stage II–IV tumors, benign and secondary metastatic tumors were near perfect for the model with and without CA125 (AUC 0.99). In comparison, the model had more difficulties discriminating between borderline and stage I tumors (AUC 0.54) and between borderline and secondary metastatic tumors (AUC 0.66). It was well able to distinguish stage II-IV cancer from other malignancies (AUCs for stage II-IV cancer versus borderline tumors was 0.92, versus stage I cancer was 0.94, and versus secondary metastatic cancer was 0.97) Table 4 Performance of the ADNEX model in polytomous discriminations between different types of adnexal mass, according to whether CA 125 level was included in the model Discrimination AUC (95% CI) P ADNEX model with CA 125 ADNEX model without CA 125 Benign vs malignant 0.97(0.96–0.98) 0.97(0.95–0.98) 0.07 Benign vs BOT 0.94(0.92–0.97) 0.94(0.91–0.97) 0.19 Benign vs Stage-I OC 0.98(0.97–0.99) 0.98(0.96–0.99) 0.21 Benign vs Stages-II–IV OC 0.99(0.99-1.00) 0.99(0.99-1.00) 0.03 Benign vs metastasis 0.99(0.98-1.00) 0.99(0.97-1.00) 0.24 BOT vs Stage-I OC 0.54(0.45–0.64) 0.50(0.41–0.60) 0.10 BOT vs Stages-II–IV OC 0.92(0.88–0.97) 0.89(0.88–0.97) 0.06 BOT vs metastasis 0.66(0.56–0.77) 0.52(0.29–0.75) 0.34 Stage-I OC vs Stages-II–IV OC 0.94(0.88–0.99) 0.88(0.80–0.96) 0.01 Stage-I OC vs metastasis 0.72(0.60–0.85) 0.54(0.21–0.86) 0.37 Stages-II–IV OC vs metastasis 0.97(0.93-1.00) 0.86(0.76–0.95) 0.01 Comparison of area under receiver–operating characteristics curve (AUC) of ADNEX model with vs without inclusion of CA 125 level using DeLong’s test. BOT, borderline ovarian tumor; OC, ovarian cancer. When including CA125 in the model, performance in discriminating between Stages II–IV OC with stage I OC and secondary metastatic tumors were improved (Tables 4 and 5 ). Validation AUCs increased from 0.88 to 0.94, p = 0.01 (stage II-IV OC vs metastatic cancer), from 0.86 to 0.97, p = 0.01 (stage II-IV OC vs stage I OC). Table 5 Performance of the ADNEX model with vs without CA 125 level in discriminating between Stage-I OC vs Stages-II–IV OC and between Stages-II–IV OC vs metastasis ADNEX model AUC (95% CI) Sensitivity (95% CI) (%) Specificity (95% CI) (%) PPV (95% CI) (%) NPV (95% CI) (%) LR+ (95% CI) LR- (95% CI) DOR * Optimal cut-off (%) P Benign vs Stages-II–IV OC With CA 125 0.99 (0.99-1.00) 100.00 (100.00-100.00) 98.97 (97.44–100.00) 92.73 (83.61–100.00) 100.00 (100.00-100.00) 97.09 (43.80-215.22) 0.00 (-) 0.00 42.95 0.03 Without CA 125 0.99 (0.99-1.00) 100.00 (100.00-100.00) 97.69 (96.15–99.23) 85.00 (77.27–94.44) 100.00 (100.00-100.00) 43.29 (22.70-82.57) 0.00 (-) 0.00 40.75 Stage-I OC vs Stages-II–IV OC With CA 125 0.88 (0.80–0.96) 80.39 (68.63–90.20) 98.53 (95.59–100.00) 97.67 (92.68–100.00) 87.01 (80.95–93.15) 54.69 (7.78-384.48) 0.20 (0.11–0.35) 273.45 36.2 0.01 Without CA 125 0.94 (0.88–0.99) 84.31 (72.55–94.12) 98.53 (95.59–100.00) 97.78 (93.18–100.00) 89.33 (82.93–95.65) 57.35 (8.17-402.77) 0.16 (0.08–0.30) 358.44 30.55 Stages-II–IV OC vs metastasis With CA 125 0.86 (0.76–0.95) 100.00 (100.00-100.00) 84.31 (72.55–92.16) 33.33 (22.22-50.00) 100.00 (100.00-100.00) 6.37 (3.50-11.61) 0.00 (-) - 31.25 0.01 Without CA 125 0.97 (0.93-1.00) 100.00 (100.00-100.00) 96.08 (90.20–100.00) 66.67 (44.44–100.00) 100.00 (100.00-100.00) 25.51 (6.56–99.24) 0.00 (-) - 15.2 Comparison of AUC of ADNEX model with vs without inclusion of CA 125 level using DeLong’s test. * Optimal cut-off, the maximum value of Youden index; AUC, area under receiver–operating characteristics curve; DOR, diagnostic odds ratio; LR+, positive likelihood ratio; LR–, negative likelihood ratio; NPV, negative predictive value; OC, ovarian cancer; PPV, positive predictive value. Discussion In our study, we show that in the hands of non-expert ultrasonographers with limited experienced, the IOTA ADNEX model can distinguish benign and malignant masses and its performance level is similar to that achieved by experienced ultrasonographers in the original ADNEX validation study published by IOTA team 12 . Regardless of whether the CA125 level is included or not, IOTA ADNEX model has excellent ability in distinguishing benign and malignant masses in a China oncology center (AUCs of 0.97 with and without CA 125). Our results are also consistent with another Chinese validation study in which the model was validated by experts ultrasonographers 16 . Except BOT vs Stage I OC, BOT vs ovarian metastases, the ADNEX model showed good to excellent performance in distinguishing most of the subtypes of adnexal masses in our study (AUC ranged from 0.72 to 0.99), especially benign tumors and stage II-IV OC (AUC 0.99), benign tumor vs ovarian metastases (AUC 0.99), BOT vs Stage II–IV OC (AUC 0.92), Stage I OC vs Stage II–IV OC (AUC 0.94) and Stage II–IV OC vs ovarian metastases(AUC 0.97) which were consistent with the results of other studies 13 , 14 , 16 , 19 . The prediction of specific subtype of malignant tumors had lower performance. When discriminating between BOT from stage I OC and between borderline and secondary metastatic tumors, AUC were 0.54 and 0.66, respectively, which are both lower than the previous research results 13 , 14 , 16 , 19 . There are many overlapping features between BOT and OC, especially early-stage OC, so it is very challenging to differentiate them in clinical practice. The survival rate of borderline ovarian tumors confined to the ovary is high, almost 100% within 10 years 20 . BOT are often affected young women, one third of them are diagnosed under 40 years old, so fertility preserving therapy should be considered 21 . A meta-analysis showed that early OC women who underwent laparoscopic surgery had a lower incidence of complications and no significant difference in recurrence rates compared with those who underwent laparotomy 22 . For non-expert ultrasonographers with limited experienced, with the help of the ADNEX model, it is helpful to identify the subtypes of ovarian tumors, except BOT vs Stage I OC, BOT vs ovarian metastases. In our validation study, using a 15% cut-off value to define malignancy, ADNEX model achieved 87.6% sensitivity and 95.9% specificity, compared with 94.5% and 78.7% in the original study 12 . Although the sensitivity decreased, the specificity increased significantly, which helps to reduce the misdiagnosis rate of noncancer patients. In our clinical practice, we can choose the appropriate cut-off value according to the needs. According to the IOTA group studies results, a 10% risk cut-off for the ADNEX model is recommended for non-oncological centers. But, because of much higher percentage of malignant cases operated in oncology centers, we probably use much higher probability cut-off levels, i.e. 37% in this study. In our population, the IOTA ADNEX model indicated high positive and negative predictive value, which are slightly higher than other validation studies 14 , 15 , thus it could be considered as an appropriate method for differentiating benign and malignant ovarian tumors in China. The ADNEX model can make more personalized diagnosis of ovarian tumors by identifying the types of malignant tumors (borderline, primary stage I, primary II- stage IV or secondary metastatic). To help clinician choose the right treatment, choose conservative treatment, or plan the most appropriate surgical procedure (laparoscopic or open surgery) when surgery is needed, or prompt doctors to find the primary site of the tumor when masses are assessed as metastatic cancer. We have shown that the ADNEX model performs equally well in the hands of non-expert ultrasonographers with limited experienced compared to the initial study, But the differential diagnosis between BOT vs Stage I OC, BOT vs ovarian metastases need to be improved. Strengths And Weaknesses The main advantage of our study is that it is the first validation study in the hands of non-expert ultrasonographers with limited experienced in China. And the researchers have successfully passed the IOTA certification test exam so that we evaluated tumor morphology strict accordance with the IOTA consensus statement and with blinding for pathology results. Every patient in our center had a preoperative CA125 measurement using the same methodology. The limitation of our study is that we are a retrospective study, which might have introduced selection bias. There are fewer cases of ovarian metastatic cancer, which can’t guarantee that the ADNEX model can draw reliable conclusions when distinguishing it from other subtypes. Conclusions The IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of non-expert ultrasonographers with limited experienced in China. Between classification different subtypes of ovarian cancers, the model has difficulty to differentiate BOT from stage I OC, BOT from ovarian metastases. Abbreviations IOTA: the International Ovarian Tumor Analysis; ADNEX model: the Assessment of Different NEoplasias in the adneXa model; BOT: borderline ovarian tumor; OC: ovarian cancer; ROC curve: Receiver-operating characteristics; AUC: the area under the curve; CA 125: Cancer antigen 125; PPV: positive predictive value; NPV: negative predictive value; LR+: positive likelihood ratio; LR-: negative likelihood ratio; Declarations Ethics approval The study was approved by the Institutional Ethics Committee of Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University. Funding Not applicable Consent for publication Not applicable. Availability of data and materials The dataset supporting the conclusions of this article is included within the article and its additional files. Competing interests The authors declare that they have no competing interests. Authors' contributions WQQ and HP devised the study and wrote the main manuscript. WQQ, HP, and DW collected the data. HP, WJJ, SC and YY performed the analyses. All authors contributed to the discussions. All authors read and approved the final manuscription. Acknowledgements Not applicable. References 1. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016; 66: 115-132. 2. Baker VV. Treatment options for ovarian cancer. Clin Obstet Gynecol. 2001; 44: 522-530. 3. Bristow RE, Chang J, Ziogas A, Anton-Culver H. Adherence to treatment guidelines for ovarian cancer as a measure of quality care. Obstet Gynecol. 2013; 121: 1226-1234. 4. Bristow RE, Chang J, Ziogas A, Randall LM, Anton-Culver H. High-volume ovarian cancer care: survival impact and disparities in access for advanced-stage disease. Gynecol Oncol. 2014; 132: 403-410. 5. Timmerman D. The use of mathematical models to evaluate pelvic masses; can they beat an expert operator? Best Pract Res Clin Obstet Gynaecol. 2004; 18: 91-104. 6. Valentin L, Hagen B, Tingulstad S, Eik-Nes S. Comparison of 'pattern recognition' and logistic regression models for discrimination between benign and malignant pelvic masses: a prospective cross validation. Ultrasound Obstet Gynecol. 2001; 18: 357-365. 7. Van Calster B, Timmerman D, Bourne T, Testa AC, Van Holsbeke C, Domali E, Jurkovic D, Neven P, Van Huffel S, Valentin L. Discrimination between benign and malignant adnexal masses by specialist ultrasound examination versus serum CA-125. J Natl Cancer Inst. 2007; 99: 1706-1714. 8. 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: 500-505. 9. Timmerman D, Testa AC, Bourne T, Ameye L, Jurkovic D, Van Holsbeke C, Paladini D, Van Calster B, Vergote I, Van Huffel S, Valentin L. Simple ultrasound-based rules for the diagnosis of ovarian cancer. Ultrasound Obstet Gynecol. 2008; 31: 681-690. 10. Timmerman D, Testa AC, Bourne T, Ferrazzi E, Ameye L, Konstantinovic ML, Van Calster B, Collins WP, Vergote I, Van Huffel S, Valentin L. Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group. J Clin Oncol. 2005; 23: 8794-8801. 11. Meys EM, Kaijser J, Kruitwagen RF, Slangen BF, Van Calster B, Aertgeerts B, Verbakel JY, Timmerman D, Van Gorp T. Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis. Eur J Cancer. 2016; 58: 17-29. 12. Van Calster B, Van Hoorde K, Valentin L, Testa AC, Fischerova D, Van Holsbeke C, Savelli L, Franchi D, Epstein E, Kaijser J, Van Belle V, Czekierdowski A, Guerriero S, Fruscio R, Lanzani C, Scala F, Bourne T, Timmerman D. Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study. BMJ. 2014; 349: g5920. 13. Szubert S, Wojtowicz A, Moszynski R, Zywica P, Dyczkowski K, Stachowiak A, Sajdak S, Szpurek D, Alcazar JL. External validation of the IOTA ADNEX model performed by two independent gynecologic centers. Gynecol Oncol. 2016; 142: 490-495. 14. Araujo KG, Jales RM, Pereira PN, Yoshida A, de Angelo AL, Sarian LO, Derchain S. Performance of the IOTA ADNEX model in preoperative discrimination of adnexal masses in a gynecological oncology center. Ultrasound Obstet Gynecol. 2017; 49: 778-783. 15. Meys E, Jeelof LS, Achten N, Slangen B, Lambrechts S, Kruitwagen R, Van Gorp T. Estimating risk of malignancy in adnexal masses: external validation of the ADNEX model and comparison with other frequently used ultrasound methods. Ultrasound Obstet Gynecol. 2017; 49: 784-792. 16. Chen H, Qian L, Jiang M, Du Q, Yuan F, Feng W. Performance of IOTA ADNEX model in evaluating adnexal masses in a gynecological oncology center in China. Ultrasound Obstet Gynecol. 2019; 54: 815-822. 17. Timmerman D, Van Calster B, Testa AC, Guerriero S, Fischerova D, Lissoni AA, Van Holsbeke C, Fruscio R, Czekierdowski A, Jurkovic D, Savelli L, Vergote I, Bourne T, Van Huffel S, Valentin L. Ovarian cancer prediction in adnexal masses using ultrasound-based logistic regression models: a temporal and external validation study by the IOTA group. Ultrasound Obstet Gynecol. 2010; 36: 226-234. 18. Prat J. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int J Gynaecol Obstet. 2014; 124: 1-5. 19. Sayasneh A, Ferrara L, De Cock B, Saso S, Al-Memar M, Johnson S, Kaijser J, Carvalho J, Husicka R, Smith A, Stalder C, Blanco MC, Ettore G, Van Calster B, Timmerman D, Bourne T. Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study. Br J Cancer. 2016; 115: 542-548. 20. Sherman ME, Mink PJ, Curtis R, Cote TR, Brooks S, Hartge P, Devesa S. Survival among women with borderline ovarian tumors and ovarian carcinoma: a population-based analysis. Cancer-Am Cancer Soc. 2004; 100: 1045-1052. 21. Trope CG, Kaern J, Davidson B. Borderline ovarian tumours. Best Pract Res Clin Obstet Gynaecol. 2012; 26: 325-336. 22. Zhang Y, Fan S, Xiang Y, Duan H, Sun L. Comparison of the prognosis and recurrence of apparent early-stage ovarian tumors treated with laparoscopy and laparotomy: a meta-analysis of clinical studies. Bmc Cancer. 2015; 15: 597. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revision 25 Aug, 2021 Review # 1 received at journal 24 Jul, 2021 Reviews received at journal 08 Jul, 2021 Reviewers invited by journal 08 Jul, 2021 Reviewer # 1 agreed at journal 07 Jul, 2021 Editor invited by journal 22 Jun, 2021 Editor assigned by journal 20 Jun, 2021 Submission checks completed at journal 20 Jun, 2021 First submitted to journal 16 Jun, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-630532","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":34343296,"identity":"c17f2bee-d7af-4327-a24d-c0f5b956516f","order_by":0,"name":"Ping He","email":"","orcid":"","institution":"Capital Medical University Beijing Obstetrics and Gynecology Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"He","suffix":""},{"id":34343297,"identity":"813a0706-5d7e-4b3d-852c-532b9d8a72ba","order_by":1,"name":"Jingjing Wang","email":"","orcid":"","institution":"Capital Medical University Beijing Obstetrics and Gynecology Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Wang","suffix":""},{"id":34343298,"identity":"560d976f-694f-43b6-a860-4eb349b1829e","order_by":2,"name":"Wei Duan","email":"","orcid":"","institution":"Capital Medical University Beijing Obstetrics and Gynecology Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Duan","suffix":""},{"id":34343299,"identity":"473a2bfa-7bbb-4bda-b3ca-974de669c3d9","order_by":3,"name":"Chao Song","email":"","orcid":"","institution":"National Health Commission of the People's Republic of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Song","suffix":""},{"id":34343300,"identity":"bb46b25f-cc26-4f23-bf9e-1e2e7e485893","order_by":4,"name":"Yu Yang","email":"","orcid":"","institution":"National Health Commission of the People's Republic of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Yang","suffix":""},{"id":34343301,"identity":"229253ef-cdcd-43a7-8c86-5c2494406852","order_by":5,"name":"Qingqing Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACZhBhAGYdgAolEK2FDaaUkBYE4DEgTgvfcd7DL94U3LHbcCPn44efOYcZ+NlzDBh+7sCtRfIwX5rlHINnyRtu5G6W7N12mEGy540BY+8Z3FoMDvOYGfMYHE42u5G7jZkRqMXgRo4BM2MbUVpynoG12BOhxfgxUIsdUAsbxBYJAlokgbYwzjE4nGB/5pkx0C/pPBJnnhUc7MWjhe/8GeMPb/4ctpdsT3744ec2azn+9uSND37i0cJwgIFNgoeBIbEByueBCOIDBxiYPwCV2eNVNApGwSgYBSMbAADomVNVa4b13wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6836-9192","institution":"Capital Medical University Beijing Obstetrics and Gynecology Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qingqing","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2021-06-16 18:01:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-630532/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-630532/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":10666361,"identity":"74af823d-8fea-4ed9-a35e-c9c5c7219126","added_by":"auto","created_at":"2021-06-22 22:55:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20685,"visible":true,"origin":"","legend":"Flowchart showing enrolment of women with adnexal mass and reasons for exclusion. *No surgery performed in 99 patients because surgery was delayed due to neoadjuvant chemotherapy (n=34), patients were in poor physical condition, unable to accept surgical treatment (n=37), patients gave up the operation for personal reasons (n=28). † Incomplete clinical data refers to missing CA125 levels.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-630532/v1/5b4779319d0830da87332ca6.png"},{"id":10666362,"identity":"175a742d-598f-4302-b0d5-dd080fe8d08e","added_by":"auto","created_at":"2021-06-22 22:55:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18192,"visible":true,"origin":"","legend":"Receiver-operating characteristics (ROC) curves for performance of International Ovarian Tumor Analysis ADNEX model in discriminating between benign and malignant adnexal masses. Optimal cut-off (the maximum value of Youden index) was 39.2% for probability of malignancy, at which sensitivity was 87.06%, specificity was 97.69%, positive predictive value was 95.03%, negative predictive value was 93.66% and area under ROC curve was 0.925. Cut-offs of 3.0%, 5.0%, 10.0% and 15.0% are also indicated.","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-630532/v1/125116ddc5db106f91592586.png"},{"id":13700205,"identity":"9d69a42b-cf10-4278-bc17-399915d13343","added_by":"auto","created_at":"2021-09-17 13:23:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":482781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-630532/v1/5955166d-f5c7-45f0-8b94-a885f3e0430a.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEstimating the Risk of Malignancy in Adnexal Masses: Validation of the ADNEX Model in the Hands of Non-expert Ultrasonographers in a Gynecological Oncology Center in China\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eIn Chinese women, the mortality rate of the three kinds of cancer is increasing year by year, including breast cancer, cervical cancer and ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, In particular, most of the patients are asymptomatic in the early stage of ovarian cancer. The five year survival rate of patients with stage III-IV ovarian cancer is less than 30%, that of patients with stage II is about 70%, and that of patients with stage I is more than 90%\u003csup\u003e2\u003c/sup\u003e. The combination of early diagnosis and timely treatment is considered to be the key factor to optimize the survival rate\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. We diagnose ovarian cancer as a benign tumor incorrectly may delay the timing of treatment and lead to inadequate treatment, on the contrary, it will make patient undergo more extensive treatment and increase the possibility of postoperative complications. It is very essential to make a correct diagnosis.\u003c/p\u003e \u003cp\u003eThe diagnosis of adnexal masses depends more on ultrasonography. Some studies have reported that subjective evaluation of a tumor by an expert ultrasonographer is an excellent method for discriminating between benign and malignant adnexal masses\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. It is necessary for doctors who are not so experienced to use a more objective method to assist in diagnosis. In order to characterize the ovarian tumors as benign or malignant, biomarkers combined with ultrasonography have been used to optimize the accuracy of diagnosis, including the risk of malignancy index (RMI). The International Ovarian Tumor Analysis group (IOTA) have presented a consensus on the terms, definitions and measurements used to describe the sonographic features of adnexal tumors\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and standardized the description of ovarian lesions. Then IOTA developed and validated many models to discriminate between benign and malignant adnexal masses such as logistic regression model LR1, LR2, Simple Rules and so on\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In a meta-analysis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, the ability of different methods to differentiate benign from malignant adnexal masses was compared. The results showed that IOTA Simple Rules and LR2 were superior to RMI and to all other methods included in the meta-analysis.\u003c/p\u003e \u003cp\u003eThe Assessment of Different NEoplasias in the adneXa (ADNEX) model is the first predictive multiclass model developed by IOTA and is able to differentiate between benign tumors, borderline tumors (BOTs), stage-I ovarian cancer (OC), stage II-IV OC and secondary metastatic ovarian cancers\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Preoperative characterization of an adnexal mass is of crucial importance for selecting the optimal management strategy and differential diagnosis of the mass by the ADNEX model may help to optimize management. In recent years, several studies have been reported the model has good to excellent performance in their populations\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Also in China, it has been reported had high accuracy in distinguishing between benign and malignant adnexal masses by expert ultrasonographers in a gynecological oncology center in Shanghai\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, there are few studies validating the discriminative performance of the ADNEX model in the hands of non-expert ultrasonographers, it has great hope as a method for the correct classification of adnexal masses by ultrasonographers with limited experience.\u003c/p\u003e \u003cp\u003eThe aim of our study was to evaluate the performance of the IOTA ADNEX model in the preoperative discrimination between benign, borderline, early and advanced stage invasive, and secondary metastatic tumors in the hands of non-expert ultrasonographers in a single oncology center in Beijing, China.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eStudy design and patients\u003c/h2\u003e\n \u003cp\u003eThis was a single center retrospective study for diagnostic accuracy conducted at a tertiary referral oncology hospital. From 1 January 2018 to 31 December 2019, seven hundred and sixty-eight patients with an ultrasound diagnosis of an adnexal mass were recruited from the Department of Ultrasound in Beijing Obstetrics and Gynecology Hospital in China consecutively.\u003c/p\u003e\n \u003cp\u003eThe inclusion criteria were as follows: (1)patients presenting with at least one adnexal mass who underwent transvaginal or transrectal ultrasonography (supplemented with transabdominal if transvaginal is not sufficient); (2) the interval between operation and ultrasonography should not exceed 120 days. (3) The patient had no previous history of ovarian cancer. The exclusion criteria were as follows: (1) Cysts that were deemed to be clearly physiological and less than 3 cm in maximum diameter; (2) Previous bilateral adnexectomy. For bilateral adnexal masses, the mass with the most complex ultrasound features was included. If both masses had similar ultrasound morphology, the largest mass or the one most easily accessible by ultrasonography was included\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The study was approved by the Institutional Ethics Committee of Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University.\u003c/p\u003e\n \u003cp\u003eTwo non-expert ultrasonographers at level 2 according to the EFSUMB classification who have successfully passed the IOTA certification test exam, assessed the sonographic tumor morphology based on the standardized manner previously published by the IOTA group\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. All assessments were done prior to obtaining pathology result, and the ultrasonographers were blinded to this outcome. The ultrasound machines used were Voluson E8 (GE Healthcare, USA) with 5.0\u0026ndash;9.0 MHz transvaginal probes and 1.0\u0026ndash;5.0 MHz transabdominal probes.\u003c/p\u003e\n \u003cp\u003eClinical and ultrasound variables of the ADNEX model were recorded. Serum CA125(U/ml) levels were assessed 7 days before surgery using an Elecsys and Cobas E analyzers (Roche, Mannheim, Germany).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReference standard\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe histopathological diagnosis of the mass after surgical removal by laparoscopy or laparotomy was used as reference standard. Tumors were staged according to the World Health Organization (WHO) classification of tumors and malignant tumors are staged using the International Federation of Obstetrics and Gynecology (FIGO) standards\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In the final diagnosis, the masses were divided into five types: benign, BOTs, stage I OC, stage II-IV OC, secondary metastatic cancer.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eAdnex Model\u003c/h2\u003e\n\u003cp\u003eWe input the variables needed by the ADNEX model into the web application ((\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.iotagroup.org/adnexmodel/\u003c/span\u003e\u003c/span\u003e). The model includes nine variables in the: age (years), serum CA125 level (U/mL), type of center (oncology referral center vs\u003c/p\u003e\n\u003cp\u003enon-oncology center), maximal diameter of the lesion (mm), maximal diameter of the largest solid part (mm), number of papillary projections (0, 1, 2, 3 or more than 3), number of cyst locules (\u0026le;\u0026thinsp;10 vs\u0026thinsp;\u0026gt;\u0026thinsp;10), acoustic shadows (yes or no), and ascites (yes or no) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. All ADNEX model parameters were logged objectively. Then the model can calculate the patient specific risk and relative risk of each subtype. With or without CA125 result, the model is able to calculate the malignant risk. This study compared the diagnostic accuracy of the model with or without CA125 result.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eWe analyzed data using R software. For statistical purposes, BOTs were considered malignant.\u003c/p\u003e\n \u003cp\u003eWe compared clinical and sonographic features of adnexal masses of the ADNEX model using the chi-square test and Fisher\u0026rsquo;s exact test for categorical data and the Mann\u0026ndash;Whitney U-test for continuous data. In order to validate the ADNEX model with and without CA125 level, receiver\u0026ndash;operating characteristics (ROC) curve analysis was performed. We calculated the area under the curve (AUC) with 95% CIs for basic discrimination between benign and malignant adnexal tumors using the total risk of malignancy (i.e., the sum of the estimated risks of the four malignant subtypes). AUCs of ADNEX model with and without CA125 level were computed for each pair of tumor types using the DeLong\u0026rsquo;s test.\u003c/p\u003e\n \u003cp\u003eWe calculated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR-) at progressive cut-off points for total risk of malignancy and at the cut-off point determined by ROC curve analyses of our data.\u003c/p\u003e\n \u003cp\u003eStatistical calculations were performed using 95% CIs, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered to be significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 1 January 2018 to 31 December 2019, 768 patients with adnexal tumors were examined by ultrasonography before laparoscopy or laparotomy. 148women were excluded from the study because of pregnancy, failure to undergo surgery, incomplete clinical data, histological diagnosis of uterine lesion, diagnosis of an extragynecological tumor. Therefore, the final cohort consisted of 620 patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAmong them, 402 (64.8%) had a benign tumor, 218 (35.2%) had a malignant tumor, including 86 (13.9%) with BOT, 75 (12.1%) with stage I OC, 53 (8.5%) with stage II-IV OC, and 4 (0.6%) with ovarian metastases. The most common benign tumors are serous cystadenoma and teratoma, while the most common malignant tumors are serous adenocarcinoma and clear cell carcinoma.\u003c/p\u003e\n\u003cp\u003eClinical and sonographic features of adnexal masses in our cohort are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The patients in the malignant group were older and had higher CA125 levels than those in the benign group. Prevalence of solid tissue, papillary projections and ascites are more common in the malignant group. Acoustic shadows are more common in benign tumor group. Besides these, prevalence of the features including the maximum diameter of the lesion and the largest solid component, more than 10 locules and presence of ascites were significant differences between benign and malignant masses. (p\u0026lt;0.05)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\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\u003eHistopathological findings in 620 women with adnexal mass\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHistological type\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en (%)\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\u003eBenign\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e402 (64.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerous cystadenoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e115 (18.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTeratoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e111 (17.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMucinous cystadenoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e81 (13.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEndometrioma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e55 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFibrothecoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e15 (2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFibroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e7 (1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdenofibroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2 (0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCystadenofibroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2 (0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParaovarian cyst\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e6 (1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMesosalpinx cyst\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4 (0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther ovarian benign lesion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4 (0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBorderline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e86 (13.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e33 (5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMucinous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e37 (6.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEndometrioid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2 (0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClear-cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1 (0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esex cord-stromal tumors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e13 (2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary Malignant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e128 (20.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerous adenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40 (6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClear cell carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e31 (4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMucinous adenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e28 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEndometrioid adenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e13 (2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerous/mucinous adenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e7 (1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarcinosarcoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3 (0.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImmature teratoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3 (0.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGranulosa-cell tumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e2 (0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSertoli leydig\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1 (0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOvarian metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4 (0.6)\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\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\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\u003eSonographic features of tumor in 620 women with adnexal mass\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;218)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenign\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBorderline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage I OC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStages-II\u0026ndash;IV OC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emetastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003etotal\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\u003eCharacteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;402)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;620)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003cp\u003e(27\u0026ndash;39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003cp\u003e(30\u0026ndash;48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003cp\u003e(41\u0026ndash;53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003cp\u003e(44\u0026ndash;57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003cp\u003e(46\u0026ndash;62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003cp\u003e(34\u0026ndash;52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCA 125 (U/mL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.4\u003c/p\u003e\n\u003cp\u003e(8\u0026ndash;17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003cp\u003e(10\u0026ndash;28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003cp\u003e(15\u0026ndash;83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e204\u003c/p\u003e\n\u003cp\u003e(53\u0026ndash;547)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003cp\u003e(28\u0026ndash;137)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003cp\u003e(13\u0026ndash;74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax diameter of lesion (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003cp\u003e(50\u0026ndash;83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003cp\u003e(53\u0026ndash;121)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106\u003c/p\u003e\n\u003cp\u003e(71\u0026ndash;148)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003cp\u003e(64\u0026ndash;143)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003cp\u003e(63\u0026ndash;108)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003cp\u003e(64\u0026ndash;133)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePresence of solid tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44(10.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63(73.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70(93.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190(30.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u0026dagger;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of largest solid\u003c/p\u003e\n\u003cp\u003ecomponent, if present (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003cp\u003e(13\u0026ndash;48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003cp\u003e(21\u0026ndash;49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003cp\u003e(31\u0026ndash;80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003cp\u003e(57\u0026ndash;79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003cp\u003e(48\u0026ndash;107)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003cp\u003e(26\u0026ndash;67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePapillary projections present\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(3.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45(52.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36(48.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28(52.8)\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\u003e109(17.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026Dagger;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e387(96.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41(47.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39(52.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(47.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109(17.6)\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\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11(2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31(36.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(30.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(15.1)\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\u003e62(10.0)\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\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2(0.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7(8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(4.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(7.5)\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\u003e14(2.3)\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\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(3.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(9.4)\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\u003e12(1.9)\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;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11(20.8)\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\u003e21(3.4)\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;\u0026thinsp;10 cyst locules\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(17.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(10.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(7.5)\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\u003e27(4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u0026dagger;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcoustic shadows\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121(30.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(6.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2(3.8)\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\u003e8(1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u0026dagger;\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\u003e3(0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11(14.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30(56.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(75.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48(7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u0026dagger;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eData are given as median (interquartile range) or n (%). P for benign vs malignant groups calculated using: *Mann\u0026ndash;Whitney U-test, \u0026dagger;chi-square test or \u0026Dagger;Fisher\u0026rsquo;s exact test. OC, ovarian cancer.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eValidation Of Iota Adnex Model\u003c/h2\u003e\n\u003cp\u003eThe diagnostic performance of the IOTA ADNEX model is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The AUC of the model to differentiate between benign and malignant adnexal masses was 0.97 (95% CI, 0.96\u0026ndash;0.98).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe performances of the IOTA ADNEX model with CA125 level at progressive cut-off points for probability of malignancy are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Sensitivity was 87.06% (82.09\u0026ndash;93.03) and specificity was 97.69% (91.03\u0026ndash;99.23) at an optimal cut-off of 39.2% probability of malignancy.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\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\u003ePerformance of the ADNEX model in discriminating between benign and malignant tumors at progressive cut-offs for probability of malignancy\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCut-off\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePPV\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNPV\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLR+\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLR-\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDOR\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\u003e3%\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\u003e97.51\u003c/p\u003e\n\u003cp\u003e(95.02\u0026ndash;99.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.49\u003c/p\u003e\n\u003cp\u003e(64.87\u0026ndash;74.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.22\u003c/p\u003e\n\u003cp\u003e(58.70-66.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.22\u003c/p\u003e\n\u003cp\u003e(96.55\u0026ndash;99.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.20\u003c/p\u003e\n\u003cp\u003e(2.75\u0026ndash;3.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003cp\u003e(0.02\u0026ndash;0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5%\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\u003e92.04\u003c/p\u003e\n\u003cp\u003e(88.05\u0026ndash;95.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.95\u003c/p\u003e\n\u003cp\u003e(84.62\u0026ndash;91.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.83\u003c/p\u003e\n\u003cp\u003e(75.30-84.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.54\u003c/p\u003e\n\u003cp\u003e(93.33\u0026ndash;97.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.64\u003c/p\u003e\n\u003cp\u003e(5.82\u0026ndash;10.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003cp\u003e(0.06\u0026ndash;0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e84.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10%\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\u003e88.06\u003c/p\u003e\n\u003cp\u003e(83.58\u0026ndash;92.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94.10\u003c/p\u003e\n\u003cp\u003e(91.79\u0026ndash;96.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.61\u003c/p\u003e\n\u003cp\u003e(84.54\u0026ndash;92.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.92\u003c/p\u003e\n\u003cp\u003e(91.71\u0026ndash;96.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.93\u003c/p\u003e\n\u003cp\u003e(10.01\u0026ndash;22.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003cp\u003e(0.09\u0026ndash;0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e114.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e15%\u003c/strong\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\u003e87.56\u003c/p\u003e\n\u003cp\u003e(82.59\u0026ndash;92.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.90\u003c/p\u003e\n\u003cp\u003e(93.85\u0026ndash;97.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.75\u003c/p\u003e\n\u003cp\u003e(88.02\u0026ndash;95.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.75\u003c/p\u003e\n\u003cp\u003e(91.57\u0026ndash;95.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.36\u003c/p\u003e\n\u003cp\u003e(13.18\u0026ndash;34.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003cp\u003e(0.09\u0026ndash;0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e164.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e39.2%\u003c/strong\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003cp\u003e(0.96\u0026ndash;0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.06\u003c/p\u003e\n\u003cp\u003e(82.09\u0026ndash;93.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.69\u003c/p\u003e\n\u003cp\u003e(91.03\u0026ndash;99.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.03\u003c/p\u003e\n\u003cp\u003e(84.07\u0026ndash;98.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.66\u003c/p\u003e\n\u003cp\u003e(91.41\u0026ndash;96.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.69\u003c/p\u003e\n\u003cp\u003e(18.09\u0026ndash;78.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003cp\u003e(0.09\u0026ndash;0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e289.92\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003e* Optimal cut-off, the maximum value of Youden index; AUC, area under receiver\u0026ndash;operating characteristics curve; DOR, diagnostic odds ratio; LR+, positive likelihood ratio; LR\u0026ndash;, negative likelihood ratio; NPV, negative predictive value; PPV, positive predictive value\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen tumors were classified into benign, BOTs, stage I OC, stage II-IV OC, secondary metastatic cancer, the model showed poor to excellent discrimination ability between the different subtypes, with AUCs varying between 0.54 and 0.99 when CA125 level was included in the model and between 0.50 and 0.99 without CA125 level (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). And AUCs of the model in differentiating benign tumor from subtypes of malignant tumor are high. The AUC was 0.94 for benign tumors compared with borderline tumors, 0.98 for benign tumors compared with stage I OC, 0.99 for benign tumors compared with stage II-IV OC, and 0.99 for benign tumors compared with secondary metastatic cancer. The ability to discriminate between benign and stage II\u0026ndash;IV tumors, benign and secondary metastatic tumors were near perfect for the model with and without CA125 (AUC 0.99). In comparison, the model had more difficulties discriminating between borderline and stage I tumors (AUC 0.54) and between borderline and secondary metastatic tumors (AUC 0.66). It was well able to distinguish stage II-IV cancer from other malignancies (AUCs for stage II-IV cancer versus borderline tumors was 0.92, versus stage I cancer was 0.94, and versus secondary metastatic cancer was 0.97)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePerformance of the ADNEX model in polytomous discriminations between different types of adnexal mass, according to whether CA 125 level was included in the model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDiscrimination\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAUC (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eADNEX model with CA 125\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eADNEX model without CA 125\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\u003eBenign vs malignant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.97(0.96\u0026ndash;0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.97(0.95\u0026ndash;0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenign vs BOT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.94(0.92\u0026ndash;0.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.94(0.91\u0026ndash;0.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenign vs Stage-I OC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.98(0.97\u0026ndash;0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.98(0.96\u0026ndash;0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenign vs Stages-II\u0026ndash;IV OC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.99(0.99-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.99(0.99-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenign vs metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.99(0.98-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.99(0.97-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBOT vs Stage-I OC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.54(0.45\u0026ndash;0.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.50(0.41\u0026ndash;0.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBOT vs Stages-II\u0026ndash;IV OC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.92(0.88\u0026ndash;0.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.89(0.88\u0026ndash;0.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBOT vs metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.66(0.56\u0026ndash;0.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.52(0.29\u0026ndash;0.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage-I OC vs Stages-II\u0026ndash;IV OC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.94(0.88\u0026ndash;0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.88(0.80\u0026ndash;0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage-I OC vs metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.72(0.60\u0026ndash;0.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.54(0.21\u0026ndash;0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStages-II\u0026ndash;IV OC vs metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.97(0.93-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.86(0.76\u0026ndash;0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eComparison of area under receiver\u0026ndash;operating characteristics curve (AUC) of ADNEX model with vs without inclusion of CA 125 level using DeLong\u0026rsquo;s test. BOT, borderline ovarian tumor; OC, ovarian cancer.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen including CA125 in the model, performance in discriminating between Stages II\u0026ndash;IV OC with stage I OC and secondary metastatic tumors were improved (Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Validation AUCs increased from 0.88 to 0.94, p\u0026thinsp;=\u0026thinsp;0.01 (stage II-IV OC vs metastatic cancer), from 0.86 to 0.97, p\u0026thinsp;=\u0026thinsp;0.01 (stage II-IV OC vs stage I OC).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePerformance of the ADNEX model with vs without CA 125 level in discriminating between Stage-I OC vs Stages-II\u0026ndash;IV OC and between Stages-II\u0026ndash;IV OC vs metastasis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eADNEX\u003c/p\u003e\n\u003cp\u003emodel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePPV\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNPV\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLR+\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLR-\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDOR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e* Optimal\u003c/p\u003e\n\u003cp\u003ecut-off\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBenign vs Stages-II\u0026ndash;IV OC\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=\"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=\"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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith CA 125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003cp\u003e(0.99-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.97\u003c/p\u003e\n\u003cp\u003e(97.44\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.73\u003c/p\u003e\n\u003cp\u003e(83.61\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.09\u003c/p\u003e\n\u003cp\u003e(43.80-215.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003cp\u003e(-)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e42.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWithout CA 125\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003cp\u003e(0.99-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.69\u003c/p\u003e\n\u003cp\u003e(96.15\u0026ndash;99.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.00\u003c/p\u003e\n\u003cp\u003e(77.27\u0026ndash;94.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.29\u003c/p\u003e\n\u003cp\u003e(22.70-82.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003cp\u003e(-)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e40.75\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eStage-I OC vs Stages-II\u0026ndash;IV OC\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=\"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=\"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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith CA 125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003cp\u003e(0.80\u0026ndash;0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.39\u003c/p\u003e\n\u003cp\u003e(68.63\u0026ndash;90.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.53\u003c/p\u003e\n\u003cp\u003e(95.59\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.67\u003c/p\u003e\n\u003cp\u003e(92.68\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.01\u003c/p\u003e\n\u003cp\u003e(80.95\u0026ndash;93.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.69\u003c/p\u003e\n\u003cp\u003e(7.78-384.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003cp\u003e(0.11\u0026ndash;0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e273.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e36.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWithout CA 125\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003cp\u003e(0.88\u0026ndash;0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.31\u003c/p\u003e\n\u003cp\u003e(72.55\u0026ndash;94.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.53\u003c/p\u003e\n\u003cp\u003e(95.59\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.78\u003c/p\u003e\n\u003cp\u003e(93.18\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.33\u003c/p\u003e\n\u003cp\u003e(82.93\u0026ndash;95.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.35\u003c/p\u003e\n\u003cp\u003e(8.17-402.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003cp\u003e(0.08\u0026ndash;0.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e358.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e30.55\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eStages-II\u0026ndash;IV OC vs metastasis\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=\"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=\"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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith CA 125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003cp\u003e(0.76\u0026ndash;0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.31\u003c/p\u003e\n\u003cp\u003e(72.55\u0026ndash;92.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.33\u003c/p\u003e\n\u003cp\u003e(22.22-50.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.37\u003c/p\u003e\n\u003cp\u003e(3.50-11.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003cp\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=\"char\"\u003e\n\u003cp\u003e31.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWithout CA 125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003cp\u003e(0.93-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.08\u003c/p\u003e\n\u003cp\u003e(90.20\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.67\u003c/p\u003e\n\u003cp\u003e(44.44\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003cp\u003e(100.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.51\u003c/p\u003e\n\u003cp\u003e(6.56\u0026ndash;99.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003cp\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=\"char\"\u003e\n\u003cp\u003e15.2\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\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003eComparison of AUC of ADNEX model with vs without inclusion of CA 125 level using DeLong\u0026rsquo;s test. * Optimal cut-off, the maximum value of Youden index; AUC, area under receiver\u0026ndash;operating characteristics curve; DOR, diagnostic odds ratio; LR+, positive likelihood ratio; LR\u0026ndash;, negative likelihood ratio; NPV, negative predictive value; OC, ovarian cancer; PPV, positive predictive value.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eIn our study, we show that in the hands of non-expert ultrasonographers with limited experienced, the IOTA ADNEX model can distinguish benign and malignant masses and its performance level is similar to that achieved by experienced ultrasonographers in the original ADNEX validation study published by IOTA team\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Regardless of whether the CA125 level is included or not, IOTA ADNEX model has excellent ability in distinguishing benign and malignant masses in a China oncology center (AUCs of 0.97 with and without CA 125). Our results are also consistent with another Chinese validation study in which the model was validated by experts ultrasonographers\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExcept BOT vs Stage I OC, BOT vs ovarian metastases, the ADNEX model showed good to excellent performance in distinguishing most of the subtypes of adnexal masses in our study (AUC ranged from 0.72 to 0.99), especially benign tumors and stage II-IV OC (AUC 0.99), benign tumor vs ovarian metastases (AUC 0.99), BOT vs Stage II\u0026ndash;IV OC (AUC 0.92), Stage I OC vs Stage II\u0026ndash;IV OC (AUC 0.94) and Stage II\u0026ndash;IV OC vs ovarian metastases(AUC 0.97) which were consistent with the results of other studies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The prediction of specific subtype of malignant tumors had lower performance. When discriminating between BOT from stage I OC and between borderline and secondary metastatic tumors, AUC were 0.54 and 0.66, respectively, which are both lower than the previous research results\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. There are many overlapping features between BOT and OC, especially early-stage OC, so it is very challenging to differentiate them in clinical practice. The survival rate of borderline ovarian tumors confined to the ovary is high, almost 100% within 10 years\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. BOT are often affected young women, one third of them are diagnosed under 40 years old, so fertility preserving therapy should be considered\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A meta-analysis showed that early OC women who underwent laparoscopic surgery had a lower incidence of complications and no significant difference in recurrence rates compared with those who underwent laparotomy\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For non-expert ultrasonographers with limited experienced,\u003c/p\u003e \u003cp\u003ewith the help of the ADNEX model, it is helpful to identify the subtypes of ovarian tumors, except BOT vs Stage I OC, BOT vs ovarian metastases.\u003c/p\u003e \u003cp\u003eIn our validation study, using a 15% cut-off value to define malignancy, ADNEX model achieved 87.6% sensitivity and 95.9% specificity, compared with 94.5% and 78.7% in the original study \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Although the sensitivity decreased, the specificity increased significantly, which helps to reduce the misdiagnosis rate of noncancer patients. In our clinical practice, we can choose the appropriate cut-off value according to the needs. According to the IOTA group studies results, a 10% risk cut-off for the ADNEX model is recommended for non-oncological centers. But, because of much higher percentage of malignant cases operated in oncology centers, we probably use much higher probability cut-off levels, i.e. 37% in this study. In our population, the IOTA ADNEX model indicated high positive and negative predictive value, which are slightly higher than other validation studies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, thus it could be considered as an appropriate method for differentiating benign and malignant ovarian tumors in China.\u003c/p\u003e \u003cp\u003eThe ADNEX model can make more personalized diagnosis of ovarian tumors by identifying the types of malignant tumors (borderline, primary stage I, primary II- stage IV or secondary metastatic). To help clinician choose the right treatment, choose conservative treatment, or plan the most appropriate surgical procedure (laparoscopic or open surgery) when surgery is needed, or prompt doctors to find the primary site of the tumor when masses are assessed as metastatic cancer. We have shown that the ADNEX model performs equally well in the hands of non-expert ultrasonographers with limited experienced compared to the initial study, But the differential diagnosis between BOT vs Stage I OC, BOT vs ovarian metastases need to be improved.\u003c/p\u003e \n\u003ch2\u003eStrengths And Weaknesses\u003c/h2\u003e\n \u003cp\u003eThe main advantage of our study is that it is the first validation study in the hands of non-expert ultrasonographers with limited experienced in China. And the researchers have successfully passed the IOTA certification test exam so that we evaluated tumor morphology strict accordance with the IOTA consensus statement and with blinding for pathology results. Every patient in our center had a preoperative CA125 measurement using the same methodology.\u003c/p\u003e \u003cp\u003eThe limitation of our study is that we are a retrospective study, which might have introduced selection bias. There are fewer cases of ovarian metastatic cancer, which can\u0026rsquo;t guarantee that the ADNEX model can draw reliable conclusions when distinguishing it from other subtypes.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThe IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of non-expert ultrasonographers with limited experienced in China. Between classification different subtypes of ovarian cancers, the model has difficulty to differentiate BOT from stage I OC, BOT from ovarian metastases.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eIOTA: the International Ovarian Tumor Analysis; ADNEX model: the Assessment of Different NEoplasias in the adneXa model; BOT: borderline ovarian tumor; OC: ovarian cancer; ROC curve: Receiver-operating characteristics; AUC: the area under the curve; CA 125: Cancer antigen 125; PPV: positive predictive value; NPV: negative predictive value; LR+: positive likelihood ratio; LR-: negative likelihood ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Ethics Committee of Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\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\u003eThe dataset supporting the conclusions of this article is included within the article and its additional files.\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\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWQQ and HP devised the study and wrote the main manuscript. WQQ, HP,\u003c/p\u003e\n\u003cp\u003eand DW collected the data. HP, WJJ, SC and YY performed the analyses. All authors contributed to the discussions. All authors read and approved the final manuscription.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016; 66: 115-132.\u003c/p\u003e\n\u003cp\u003e2. Baker VV. Treatment options for ovarian cancer. Clin Obstet Gynecol. 2001; 44: 522-530.\u003c/p\u003e\n\u003cp\u003e3. Bristow RE, Chang J, Ziogas A, Anton-Culver H. Adherence to treatment guidelines for ovarian cancer as a measure of quality care. Obstet Gynecol. 2013; 121: 1226-1234.\u003c/p\u003e\n\u003cp\u003e4. Bristow RE, Chang J, Ziogas A, Randall LM, Anton-Culver H. High-volume ovarian cancer care: survival impact and disparities in access for advanced-stage disease. Gynecol Oncol. 2014; 132: 403-410.\u003c/p\u003e\n\u003cp\u003e5. Timmerman D. The use of mathematical models to evaluate pelvic masses; can they beat an expert operator? Best Pract Res Clin Obstet Gynaecol. 2004; 18: 91-104.\u003c/p\u003e\n\u003cp\u003e6. Valentin L, Hagen B, Tingulstad S, Eik-Nes S. Comparison of \u0026apos;pattern recognition\u0026apos; and logistic regression models for discrimination between benign and malignant pelvic masses: a prospective cross validation. Ultrasound Obstet Gynecol. 2001; 18: 357-365.\u003c/p\u003e\n\u003cp\u003e7. Van Calster B, Timmerman D, Bourne T, Testa AC, Van Holsbeke C, Domali E, Jurkovic D, Neven P, Van Huffel S, Valentin L. Discrimination between benign and malignant adnexal masses by specialist ultrasound examination versus serum CA-125. J Natl Cancer Inst. 2007; 99: 1706-1714.\u003c/p\u003e\n\u003cp\u003e8. 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: 500-505.\u003c/p\u003e\n\u003cp\u003e9. Timmerman D, Testa AC, Bourne T, Ameye L, Jurkovic D, Van Holsbeke C, Paladini D, Van Calster B, Vergote I, Van Huffel S, Valentin L. Simple ultrasound-based rules for the diagnosis of ovarian cancer. Ultrasound Obstet Gynecol. 2008; 31: 681-690.\u003c/p\u003e\n\u003cp\u003e10. Timmerman D, Testa AC, Bourne T, Ferrazzi E, Ameye L, Konstantinovic ML, Van Calster B, Collins WP, Vergote I, Van Huffel S, Valentin L. Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group. J Clin Oncol. 2005; 23: 8794-8801.\u003c/p\u003e\n\u003cp\u003e11. Meys EM, Kaijser J, Kruitwagen RF, Slangen BF, Van Calster B, Aertgeerts B, Verbakel JY, Timmerman D, Van Gorp T. Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis. Eur J Cancer. 2016; 58: 17-29.\u003c/p\u003e\n\u003cp\u003e12. Van Calster B, Van Hoorde K, Valentin L, Testa AC, Fischerova D, Van Holsbeke C, Savelli L, Franchi D, Epstein E, Kaijser J, Van Belle V, Czekierdowski A, Guerriero S, Fruscio R, Lanzani C, Scala F, Bourne T, Timmerman D. Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and \u0026nbsp;secondary metastatic tumours: prospective multicentre diagnostic study. BMJ. 2014; 349: g5920.\u003c/p\u003e\n\u003cp\u003e13. Szubert S, Wojtowicz A, Moszynski R, Zywica P, Dyczkowski K, Stachowiak A, Sajdak S, Szpurek D, Alcazar JL. External validation of the IOTA ADNEX model performed by two independent gynecologic centers. Gynecol Oncol. 2016; 142: 490-495.\u003c/p\u003e\n\u003cp\u003e14. Araujo KG, Jales RM, Pereira PN, Yoshida A, de Angelo AL, Sarian LO, Derchain S. Performance of the IOTA ADNEX model in preoperative discrimination of adnexal masses in a gynecological oncology center. Ultrasound Obstet Gynecol. 2017; 49: 778-783.\u003c/p\u003e\n\u003cp\u003e15. Meys E, Jeelof LS, Achten N, Slangen B, Lambrechts S, Kruitwagen R, Van Gorp T. Estimating risk of malignancy in adnexal masses: external validation of the ADNEX model and comparison with other frequently used ultrasound methods. Ultrasound Obstet Gynecol. 2017; 49: 784-792.\u003c/p\u003e\n\u003cp\u003e16. Chen H, Qian L, Jiang M, Du Q, Yuan F, Feng W. Performance of IOTA ADNEX model in evaluating adnexal masses in a gynecological oncology center in China. Ultrasound Obstet Gynecol. 2019; 54: 815-822.\u003c/p\u003e\n\u003cp\u003e17. Timmerman D, Van Calster B, Testa AC, Guerriero S, Fischerova D, Lissoni AA, Van Holsbeke C, Fruscio R, Czekierdowski A, Jurkovic D, Savelli L, Vergote I, Bourne T, Van Huffel S, Valentin L. Ovarian cancer prediction in adnexal masses using ultrasound-based logistic regression models: a temporal and external validation study by the IOTA group. Ultrasound Obstet Gynecol. 2010; 36: 226-234.\u003c/p\u003e\n\u003cp\u003e18. Prat J. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int J Gynaecol Obstet. 2014; 124: 1-5.\u003c/p\u003e\n\u003cp\u003e19. Sayasneh A, Ferrara L, De Cock B, Saso S, Al-Memar M, Johnson S, Kaijser J, Carvalho J, Husicka R, Smith A, Stalder C, Blanco MC, Ettore G, Van Calster B, Timmerman D, Bourne T. Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study. Br J Cancer. 2016; 115: 542-548.\u003c/p\u003e\n\u003cp\u003e20. Sherman ME, Mink PJ, Curtis R, Cote TR, Brooks S, Hartge P, Devesa S. Survival among women with borderline ovarian tumors and ovarian carcinoma: a population-based analysis. Cancer-Am Cancer Soc. 2004; 100: 1045-1052.\u003c/p\u003e\n\u003cp\u003e21. Trope CG, Kaern J, Davidson B. Borderline ovarian tumours. Best Pract Res Clin Obstet Gynaecol. 2012; 26: 325-336.\u003c/p\u003e\n\u003cp\u003e22. Zhang Y, Fan S, Xiang Y, Duan H, Sun L. Comparison of the prognosis and recurrence of apparent early-stage ovarian tumors treated with laparoscopy and laparotomy: a meta-analysis of clinical studies. Bmc Cancer. 2015; 15: 597.\u003c/p\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":"ADNEX model, CA 125, diagnosis, ovarian tumor, ultrasonography","lastPublishedDoi":"10.21203/rs.3.rs-630532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-630532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe diagnosis of adnexal masses depends more on ultrasonography. This study aim to validate the diagnostic accuracy of the International Ovarian Tumor Analysis (IOTA) ADNEX model in the preoperative diagnosis of adnexal masses in the hands of non-expert ultrasonographers in a gynecological oncology center in China.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis was a single oncology center, retrospective diagnostic accuracy study from 620 patients. All patients underwent surgery and the histopathological diagnosis was used as reference standard. The masses were divided into five types according to the ADNEX model: benign ovarian tumor, borderline ovarian tumor (BOT), Stage-I ovarian cancer (OC), Stages-II-IV OC and ovarian metastasis. Receiver-operating characteristics (ROC) curve analysis was used to evaluate the ability of the ADNEX model to classify tumors into different histological types with and without Cancer antigen 125 (CA 125) results.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOf the 620 women, 402 (64.8%) had a benign ovarian tumor and, 218 (35.2%) had a malignant ovarian tumor, including 86 (13.9%) with BOT, 75 (12.1%) with Stage-I OC, 53 (8.5%) with Stages-II-IV OC and 4 (0.6%) with ovarian metastasis. The AUC of the model to differentiate between benign and malignant adnexal masses was 0.97 (95% CI, 0.96\u0026ndash;0.98). Performance was excellent for the discrimination between benign vs Stage II-IV OC, benign vs ovarian metastasis with AUCs of 0.99 (95% CI, 0.99-1.00) and 0.99 (95% CI, 0.98-1.00), respectively. Performance of the model was less effective at distinguishing between BOT and Stage I OC and between BOT and ovarian metastasis, with AUC of 0.54 (95% CI, 0.45\u0026ndash;0.64) and 0.66 (95% CI, 0.56\u0026ndash;0.77), respectively. When including CA125 in the model, performance in discriminating between Stages II\u0026ndash;IV OC with stage I OC and ovarian metastasis were improved (AUC increased from 0.88 to 0.94, P\u0026thinsp;=\u0026thinsp;0.01; 0.86 to 0.97, p\u0026thinsp;=\u0026thinsp;0.01, respectively).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThe IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of non-expert ultrasonographers with limited experienced in China. Between classification different subtypes of ovarian cancers, the model has difficulty to differentiate BOT from stage I OC, BOT from ovarian metastases.\u003c/p\u003e","manuscriptTitle":"Estimating the Risk of Malignancy in Adnexal Masses: Validation of the ADNEX Model in the Hands of Non-expert Ultrasonographers in a Gynecological Oncology Center in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-22 22:55:10","doi":"10.21203/rs.3.rs-630532/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2021-08-25T06:17:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-07-25T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-07-08T04:33:07+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-07-08T04:10:34+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-07-08T00:00:00+00:00","index":1,"fulltext":""},{"type":"editorInvited","content":"","date":"2021-06-22T23:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-06-21T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-06-20T15:59:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2021-06-16T04:14:52+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":"1af7115f-4041-4de1-a3d0-1fd37ce37244","owner":[],"postedDate":"June 22nd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":5204652,"name":"Obstetrics \u0026 Gynecology"},{"id":5204653,"name":"Oncology"}],"tags":[],"updatedAt":"2021-11-18T03:10:12+00:00","versionOfRecord":[],"versionCreatedAt":"2021-06-22 22:55:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-630532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-630532","identity":"rs-630532","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}