Individualized risk prediction nomogram and ultrasound signs for preoperative differentiation of struma ovarii

Individualized risk prediction nomogram and ultrasound signs for preoperative differentiation of struma ovarii | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Individualized risk prediction nomogram and ultrasound signs for preoperative differentiation of struma ovarii Wanting Chen, Fei Ji, Weihan Xiao, Jiajia Tang, Wenjing Zhao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9459948/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objectives Struma ovarii (SO) often presents as a multilocular-solid adnexal mass with abundant vascularity, easily misdiagnosed as an epithelial malignancy and leading to inappropriate surgical management. This study aimed to develop and validate a preoperative nomogram to differentiate O-RADS US 4/5 struma ovarii (SO) from early-stage high-grade serous carcinoma (HGSC) and to identify characteristic ultrasound signs of SO, enhancing diagnostic accuracy and clinical applicability. Methods This retrospective cohort study enrolled 93 SO lesions from 92 patients and 87 early-stage HGSC lesions from 79 patients at Peking Union Medical College Hospital between January 2017 and December 2025. Lesions were randomly divided into training and validation sets (7:3). Independent predictors were identified using univariate and multivariate logistic regression. An individualized risk prediction nomogram was then developed, along with a dynamic nomogram constructed using the "DynNom" and "shiny" packages in R. Model performance was evaluated using the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis. Results Among SO cases, 65 (70.7%) were asymptomatic, 23 (25.0%) had elevated CA125 (> 35 U/mL). Ascites was present in 7 patients (7.6%). 80 lesions (86.0%) were classified as O-RADS 5, and 25 (26.9%) misdiagnosed as malignant. Most SO were pure lesions with low malignant transformation and no metastasis. 9 premenopausal patients (17.3%) underwent radical surgery, suggesting overtreatment. Multivariate logistic regression identified CA125, heterogeneous cyst fluid, comet-tail artifact, morphological pattern of the solid component, and echogenicity of the solid component as independent preoperative predictors. The clinical-ultrasound nomogram demonstrated excellent predictive performance, with an AUC of 0.971 (95% CI: 0.95–0.99) in the training set and 0.939 (95% CI: 0.88–0.99) in the validation set. Calibration curves indicated strong agreement between predicted and observed probabilities, and decision-curve analysis confirmed its clinical utility. Three typical ultrasound signs of SO—ovary-like sign, palette sign, and Cheerios sign—were summarized, and the interpretation of the “pearl sign” was refined. Conclusion The static and dynamic nomograms and ultrasound signs enable accurate preoperative differentiation of O-RADS US 4/5 SO from HGSC, which provide an innovative and reproducible framework for precise preoperative decision-making. Struma ovarii high-grade serous ovarian carcinoma ultrasound signs nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background Struma ovarii (SO) is a rare, highly differentiated monodermal teratoma defined by thyroid tissue comprising more than 50% of the tumor [ 1 , 2 ], accounting for 2–3% of teratomas and 0.5-1% of ovarian tumors [ 3 – 5 ]. Although malignant transformation occurs in 5–10% of cases, prognosis is generally favorable in both benign and nonmetastatic cases [ 6 – 8 ]. Clinical presentation is often nonspecific, asymptomatic patients may be incidentally identified during routine physical examinations or imaging screenings, which substantially increases the difficulty of preoperative diagnosis [ 9 ]. Due to its rarity, existing evidence primarily comes from case reports and small retrospective series, and standardized diagnostic and management guidelines are still lacking [ 10 , 11 ]. Accurate preoperative diagnosis is crucial for appropriate management plans. Ultrasound (US) is the first-line imaging modality for adnexal masses. The Ovarian-Adnexal Reporting and Data System (O-RADS) US offers a standardized assessment framework for clinical practice [ 12 ]. However, SO often presents as a multilocular-solid mass with moderate to abundant blood flow signals on ultrasound,, classified as O-RADS 4/5 [ 13 , 14 ]. magnetic resonance imaging (MRI) may show lobulated cystic-solid lesions with a "stained-glass" appearance. High signal intensity on T1-weighted images or punctate low-signal foci on T2-weighted images may also be observed [ 15 , 16 ]. Additionally, high-risk time-intensity curve patterns without diffusion restriction [ 17 , 18 ] lead to O-RADS MRI 5 [ 17 , 18 ]. These features overestimate malignancy risk and lack reliable discriminatory value [ 11 ]. Approximately 90% of ovarian cancers are epithelial malignancies, 70% -80% of which are high-grade serous ovarian cancers (HGSC), the most common and aggressive subtype [ 19 ]. Early-stage (FIGO I-II) HGSC often appears as O-RADS US 4/-5 complex cystic-solid mass with abundant blood flow. Thus, HGSC was chosen as the primary comparator for SO due to its high incidence, making their differentiation clinically critical. More importantly, preoperative differentiation directly guides surgical strategy and patient benefit. Early-stage HGSC requires standardized staging surgery and systemic treatment [ 19 , 20 ], whereas SO can generally be managed conservatively [ 21 , 22 ]. Currently, no imaging-based prediction model specifically exists to preoperatively differentiate SO from early-stage ovarian cancer, and O-RADS US is insufficient. To fill this gap, we retrospectively analyzed O-RADS 4/5 SO lesions and FIGO stage I-II HGSC to develop and validate an individualized preoperative risk prediction nomogram and an interactive dynamic nomogram. We also propose three characteristic ultrasound signs for SO, which may serve as valuable imaging clues for differentiating SO from malignant ovarian cancers. This study has the potential to enhance the accuracy of preoperative risk stratification and provide a more reliable basis for surgical planning and clinical decision-making. 2. Methods 2.1 Patients This retrospective study included patients with gynecologic surgery for ovarian masses at Peking Union Medical College Hospital between January 2017 and December 2025. Inclusion criteria were: (1) complete preoperative clinical data and evaluable ultrasound images; (2) O-RADS US 4/5 cystic-solid lesions pathologically confirmed as SO or FIGO stage I-II HGSC postoperatively. Exclusion criteria were: (1) preoperative treatment for ovarian tumors; (2) O-RADS US 1–3 lesions; and (3) incomplete clinical data or non-evaluable ultrasound images. After screening, 93 SO lesions from 92 patients and 87 early stage HGSC lesions from 79 patients were included. This retrospective study followed the Declaration of Helsinki and received approval from the Institutional Review Board of our hospital (IRB No. I-24PJ0809). 2.2 Clinical variables Given their availability in routine practice and predictive value, key variables were from the electronic medical record system, including: (1) age; (2) family history (yes/no), breast/ovarian/gynecologic malignancies in first/second-degree relatives; (3) symptoms (yes/no), abdominal pain, distension, irregular vaginal bleeding, or palpable abdominal mass; and (4) CA125 > 35 U/mL (yes/no). All data were collated using standardized collection templates and independently verified by two investigators. Comorbidities and surgical approach were additionally recorded. 2.3 Ultrasound Examinations and Feature Preoperative ultrasound examinations were performed by certified radiologists using standardized equipment including GE Voluson E10 (GE Healthcare, USA), Philips EPIQ 7 (Philips Healthcare, the Netherlands), and Philips iU22, with transabdominal (2.5-5.0 MHz) and transvaginal (5.0–9.0 MHz) probes. Each lesion was assessed in multiple planes, and at least two orthogonal grayscale and color Doppler images were stored. Lesions were selected and classified as according to the O-RADS US 2022 [ 12 ]. Based on expert consensus derived from extensive clinical experience, the following ultrasound variables were analyzed: (1) laterality (unilateral/bilateral); (2) largest diameter in any plane; (3) contour (regular/irregular); (4) number of locules (1, 2–4 or ≥ 5); (5) regularity of locules (yes/no); (6) homogeneity of cyst fluid (yes/ no); (7) comet_tail artifact (yes/no); (8) morphological pattern of the solid component (intracystic solid component, extracystic solid tissue or predominantly solid component). Intracystic solid component denotes a papillary projection or nodule (height ≥ 3 mm) from the cyst wall or septation protruding into the cyst cavity. Extracystic solid tissue refers to solid components distributed along the periphery of locules, presenting as ring-like or septal-like focal thickening. Predominantly solid component refers to a lesion composed of at least 80% solid tissue, with minor cystic components. (9) echogenicity of solid component (hypoechoic, isoechoic, or hyperechoic), relative to the myometrium; (10) color score (grade 0–1, 2 or 3) (11) calcification (yes/no); and (12) free intraperitoneal fluid (yes/no). All images were independently reviewed by two radiologists with over 10 years of experience in gynecological imaging. Discrepancies were confirmed by a senior expert with more than 20 years of clinical experience. All images were de -identified and randomly anonymized before analysis. 2.4 Statistical analysis All statistical analyses were conducted using R (version 4.3.1) and SPSS (version 26.0). The cohort was randomly allocated into a training set (n = 126) and a validation set (n = 54) at a 7:3 ratio. Continuous variables were expressed as median (interquartile range) and group comparisons using the t test or Mann-Whitney U-test. Categorical variables were expressed as n (%) and compared between groups using the chi-square test or Fisher’s exact test. Univariate and multivariate logistic regression identified independent predictors to develop a clinical-ultrasound nomogram for individualized risk prediction. Furthermore, a dynamic nomogram was constructed using the "DynNom" and "shiny" packages in R. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) were used to evaluate the predictive performance and clinical utility of the model. Statistical tests were two-tailed and significance was set at P < 0.05. 3. Result 3.1 Participant characteristics Table 1 summarizes the baseline clinical and ultrasonographic characteristics of the training and validation cohorts, with no statistically significant differences observed (P > 0.05). Detailed clinical and ultrasonographic characteristics and ultrasonographic features of SO patients and lesions are provided in Tables 2 and 3 , respectively. Among SO cases, 65 patients (70.7%) were asymptomatic and incidentally detected during physical examinations or imaging studies. Notably, 3 patients (3.3%) were diagnosed during pregnancy. Laboratory findings revealed elevated CA125 levels (> 35 U/mL) in 23 patients (25.0%) and thyroid dysfunction in 12 patients (13.0%). Ascites was present in 7 patients (7.6%), fluid outside the pouch of Douglas. All assessed lesions were cystic-solid masses. 13 lesions (14.0%) were classified as O-RADS 4, and 80 lesions (86.0%) as O-RADS 5. In the initial ultrasonographic reports, 25 lesions (26.9%) were misclassified as malignant, and 37 lesions (39.8%) could not be definitively characterized. Postoperative pathological analysis revealed 69 cases (75.0%) of pure SO, 20 cases (21.7%) of SO combined with dermoid cyst, and 3 cases (3.3%) of SO combined with cystadenoma. Among these, 4 cases (4.3%) were malignant SO, 2 cases (2.2%) were carcinoid tumors, and 2 cases (2.2%) were microfocal papillary carcinoma, with no distant or local metastasis observed in any case. Regarding surgical management, 52 of 92 SO patients (56.5%) were premenopausal. Among these premenopausal patients, 9 (17.3%) underwent hysterectomy with bilateral salpingo-oophorectomy (BSO), including 3 who underwent open hysterectomy with BSO, which suggested potential overtreatment in some cases. Table 1 Baseline characteristics in the training and test cohorts Characteristics Training (N = 126) Testing (N = 54) P Age 52.00 (44.00–62.00) 51.50 (44.00-59.75) 0.713 Largest_diameter 7.10 (5.70–10.00) 6.75 (5.62–9.95) 0.670 Family_history No 109 (86.5%) 44 (81.5%) 0.524 Yes 17 (13.5%) 10 (18.5%) Symptom No 82 (65.1%) 33 (61.1%) 0.735 Yes 44 (34.9%) 21 (38.9%) O-RADS US 4 17 (13.5%) 6 (11.1%) 0.846 5 109 (86.5%) 48 (88.9%) Ultrasound_assessment Benign 25 (19.8%) 7 (13.0%) 0.542 Malignant 45 (35.7%) 21 (38.9%) Uncertain 56 (44.4%) 26 (48.1%) Laterality Unilateral 112 (88.9%) 50 (92.6%) 0.626 Bilateral 14 (11.1%) 4 (7.4%) Data are given as n (%) or median (interquartile range). ORADS, the Ovarian-Adnexal Reporting and Data System. Table 2 Clinical characteristics of women with SO (N = 92). Variable Value Age 49.50(38.50, 61.00) Menopausal No 52 (56.5%) Yes 40 (43.5%) Pregnant No 89 (96.7%) Yes 3 (3.3%) Abdominal_pain No 77 (83.7%) Yes 15 (16.3%) Abdominal_distension No 74 (80.4%) Yes 18 (19.6%) Irregular_bleeding No 86 (93.5%) Yes 6 (6.5%) Abdominal_mass No 89 (96.7%) Yes 3 (3.3%) Ascites No 85(92.4%) Yes 7(7.6%) Pregnant No 89 (96.7%) Yes 3 (3.3%) CA125 ≤ 35 U/mL 69 (75.0%) > 35 U/mL 23 (25.0%) Thyroid_dysfunction No 80 (87.0%) Yes 12 (13.0%) Concomitant_disease No 69 (75.0%) Dermoid cyst 20 (21.7%) Cystadenoma 3 (3.3%) Surgical_approach Laparoscopic conservative surgery 45 (48.9%) Laparoscopic hysterectomy with BSO 27 (29.3%) Open hysterectomy with BSO 19 (21.8%) Data are given as n (%) or median (interquartile range). Clinical and laboratory variables were collected at the time of SO diagnosis. BSO: bilateral salpingo-oophorectomy. Table 3 Ultrasonic features of the SO lesions (N = 93). Variable Value Largest_diameter 6.40(4.90, 9.00) O-RADS US 4 13 (14.0%) 5 80 (86.0%) Subjective ultrasound assessment Benign 31 (33.3%) Malignant 25 (26.9%) Uncertain 37 (39.8%) Data are given as n (%) or median (interquartile range). 3.2 Clinical and ultrasound predictors for two diseases Table 4 presents a comparative clinical indicators and ultrasound parameters of SO and early-stage HGSC in the training set. There were significant differences between the two groups for several variables, including CA125 (p = 0.001), heterogeneity of cyst fluid (p = 0.025), comet_tail artifact (p = 0.036), morphological pattern of the solid component (p = 0.03), and echogenicity of solid components (p = 0.01). Table 4 Univariate and multivariate analyses of clinical and ultrasound indicators of SO and HGSC in training set. Parameter Univariate analysis Multivariate analysis HGSC SO P OR 95%CI P Age 54.3 ± 8.9 48.3 ± 13.7 0.006 0.93 0.85–1.01 0.096 Family_history 0.053 No 48 (77.4%) 57 (90.5%) Yes 14 (22.6%) 6 (9.5%) Symptom 0.087 No 33 (53.2%) 43 (68.3%) Yes 29 (46.8%) 20 (31.7%) CA125 35 U/mL 52 (83.9%) 13 (20.6%) Largest_diameter 8.4 ± 3.8 7.3 ± 3.2 0.096 Laterality 0.011 0.01 0.01-1308036.50 0.622 Unilateral 50 (80.6%) 62 (98.4%) Bilateral 12 (19.4%) 1 (1.6%) Vascularity 0.838 No/Minimal 5 (8.1%) 7 (11.1%) Moderate 17 (27.4%) 16 (25.4%) Abundant 40 (64.5%) 40 (63.5%) Contour 0.172 regular 36 (58.1%) 44 (69.8%) irregular 26 (41.9%) 19 (30.2%) Locular number 0.105 ≥ 5 24 (38.7%) 33 (52.4%) 0.010 2–4 30 (48.4%) 28 (44.4%) 0.047 1 8 (12.9%) 2 (3.2%) 0.036 Cystic regularity < 0.001 0.505 No 34 (59.6%) 20 (29%) Yes 23 (40.4%) 49 (71%) Cystic echogenicity 0.004 0.08 0.01–0.73 0.025 heterogeneous 20 (35.1%) 42 (60.9%) homogeneous 37 (64.9%) 27 (39.1%) Comet-tail artifact < 0.001 7.01 1.13–43.54 0.036 No 55 (88.7%) 32 (50.8%) Yes 7 (11.3%) 31 (49.2%) Solid pattern < 0.010 0.030 Intracystic solid component 28 (45.2%) 19 (30.2%) Ref Extracystic solid component 8 (12.9%) 36 (57.1%) 13.49 1.53-118.52 0.019 Predominantly solid mass 26 (41.9%) 8 (12.7%) 0.94 0.14–6.27 0.953 Solid echogenicity < 0.010 0.010 Hypoechoic 2 (3.2%) 40 (63.5%) Ref Isoechoic 31 (50.0%) 20 (31.7%) 0.03 0.01–0.15 < 0.001 Hyperechoic 29 (46.8%) 3 (4.8%) 0.01 0.01–0.03 < 0.001 Calcification 0.654 No 53 (85.5%) 52 (82.5%) Yes 9 (14.5%) 11 (17.5%) Free intraperitoneal fluid 0.239 No 36 (58.1%) 43 (68.3%) Yes 26 (41.9%) 20 (31.7%) Data are given as n (%) or median (interquartile range). CI, confidence interval; OR, odds ratio. Table 5 Diagnostic performance for differentiating SO from early-stage HGSC. Training Cohort AUC (95% CI) ACC (95% CI) SEN (95% CI) SPE (95% CI) PPV (95% CI) NPV (95% CI) 0.971 (0.949–0.994) 0.92 (0.919–0.921) 0.952 (0.900-1.000) 0.887 (0.808–0.966) 0.896 (0.822–0.969) 0.948 (0.891-1.000) Testing Cohort 0.939 (0.882–0.995) 0.873 (0.869–0.877) 0.800 (0.657–0.943) 0.960 (0.883-1.000) 0.960 (0.883-1.000) 0.800 (0.657–0.943) ACC , accuracy; AUC , area under the Curve; ACC , accuracy; SEN , sensitivity; SPE , specificity; NPV , negative predictive value; PPV , positive predictive value. 3.3 Model performance and validation Using independent predictors derived from multivariable regression, a combined clinical-ultrasound nomogram was established to predict the probability of SO and early-stage HGSC (Fig. 1 a). As shown in Table 5 , the AUC was 0.971 (95%CI, 0.949–0.994) in the training cohort and 0.939 (95%CI, 0.882–0.995) in the testing cohort, with corresponding accuracy values of 0.920 (95%CI, 0.919–0.921) and 0.873 (95%CI, 0.869–0.877), respectively, indicating excellent discriminative ability between two diseases (Fig. 1 b). Calibration curves demonstrated great agreement between predicted and observed probabilities in both cohorts, confirming satisfactory calibration (Fig. 1 c). DCA showed favorable net clinical benefit across a wide range of threshold probabilities, validating its clinical utility for differentiating SO from early-stage HGSC (Fig. 1 d, e). Table 5 Diagnostic performance for differentiating SO from early-stage HGSC. Training Cohort AUC (95% CI) ACC (95% CI) SEN (95% CI) SPE (95% CI) PPV (95% CI) NPV (95% CI) 0.971 (0.949–0.994) 0.92 (0.919–0.921) 0.952 (0.900-1.000) 0.887 (0.808–0.966) 0.896 (0.822–0.969) 0.948 (0.891-1.000) Testing Cohort 0.939 (0.882–0.995) 0.873 (0.869–0.877) 0.800 (0.657–0.943) 0.960 (0.883-1.000) 0.960 (0.883-1.000) 0.800 (0.657–0.943) ACC , accuracy; AUC , area under the Curve; ACC , accuracy; SEN , sensitivity; SPE , specificity; NPV , negative predictive value; PPV , positive predictive value. 3.4 Utilization of individual prediction nomogram To illustrate the clinical application workflow of the individualized risk prediction nomogram, this study enrolled four newly diagnosed cases of SO and early-stage HGSC respectively. We extracted their clinical and ultrasound variables and summed the scores corresponding to each predictive variable in the nomogram. Finally, the probability of SO and early-stage HGSC for each patient was calculated. The pathological diagnosis of SO in cases with higher predicted probabilities supports the accuracy of the model. This nomogram allows for an intuitive, quantifiable risk assessment and serves as a reference for preoperative risk stratification and clinical decision-making (Fig. 2 ). 3.5 Dynamic nomogram for Predicting SO Building on the traditional risk prediction nomogram for distinguishing SO from early-stage HGSC, we created a dynamic online nomogram to enable more efficient and convenient individualized risk assessment. The website is: https://sopredict.shinyapps.io/Ovarian_Nomogram_App/( Figure S1 ). 3.6 The pathological basis of the morphological pattern of the solid component The morphological pattern of the solid component differs significantly between SO and HGSC. In SO, the solid component is located along the cystic cavities. Although some larger cystic cavities may cause the intervening solid tissue to appear to protrude into the cavity, which can be mistakenly identified as papillary projections, three-dimensional (3D) ultrasound can more intuitively clarify their spatial relationship and highlight the hyperechoic features of the solid components (Fig. 3 a-d). Careful observation usually reveals a continuous and smooth cyst wall between the solid components and the cyst cavity, which contrasts with the genuine papillary projections or mural nodules in HGSC, where the intracystic excrescences disrupt the smooth inner wall (Fig. 3 e-h). In addition, MRI also supported this observation. The signal within SO cystic cavities can present a "stained glass appearance" due to variations in colloid viscosity and concentration, and cyst wall or septa may show enhancement, but no obvious enhancement of solid components within the cystic cavities has been described [ 18 , 23 ]. Pathologically, these imaging differences reflect distinct tissue origins. SO presents as a solid mass with scattered round or oval cystic cavities lined by smooth walls and filled with abundant colloid-like material. The solid components of SO correspond to thyroid tissue consisting of follicular epithelium and vascular-rich fibrous stroma. These cystic cavities tend to originate from thyroid follicles or cystic degeneration, lack solid components [ 4 , 24 , 25 ]. In contrast, HGSC commonly appears as grayish-yellow solid mass with hemorrhage, necrosis, and irregular cystic cavities (Fig. 4 ). It is characterized by high-grade malignant epithelial proliferation with fibrovascular cores, and papillae and mural nodules may project into cystic spaces and show invasive growth [ 26 – 28 ]. 3.7 Characteristic ultrasonic signs of SO To enhance model interpretability, we summarized three characteristic ultrasound signs of SO, all of which align with the ultrasound variables selected by the model. They share the following common characteristics: the isoechoic to hyperechoic solid components are distributed peripherally or between cystic cavities without intracystic protrusion, forming stable peripheral scaffold-like or ring-liked structures. The cyst contents are heterogeneous and occasional show comet-tail artifact. The three specific ultrasonographic features include: (1) Ovary-like sign : Multiple round or oval cystic locules within the solid component form an ovary-like appearance; (2) palette sign : a well-defined solid core surrounded by multiple cysts containing heterogeneous contents, creating a petal configuration; (3) Cheerios sign : A well-circumscribed, hyperechoic solid area with a central cyst within a multilocular-solid mass. (Fig. 5 ). 3.8 Reinterpretation of the Pearl Sign Previous studies have suggested that the "pearl sign" is a specific ultrasonographic feature of SO, corresponding to the "petal sign" proposed above. It is described as a smooth roundish solid area with vascularization [ 13 , 14 , 29 ]. In our cases, the hyperechoic solid components in SO are typically peripheral to cystic cavities with moderate to intense vascularity, as they derive from thyroid tissue (Fig. 6 a,b). Notably, the "pseudopearl sign" from deposits arises from the gradual deposition and compaction of intracystic colloid over time, appearing as hypoechoic or hyperechoic flocculent aggregates and may be misinterpreted as the "pearl sign"(Fig. 6 e-h) [ 27 , 30 ]. Unlike true thyroid tissue, these colloid deposits may show slight deformation or displacement with postural changes or pressure from the operator and generally show no internal blood flow signals on color Doppler ultrasound(Fig. 6 c,d). The IOTA group categorized such deposits as a pitfall in distinguishing intracystic solid components and recommended, Doppler and dynamic scanning to reduce errors [ 31 ]. Distinguishing these signs is critical to minimize misdiagnosis and validate the "pearl sign" specificity for SO. Only excluding colloid deposition can reliably assess its true value. 4. Discussion Currently, cystic-solid SO lesions are frequently classified as O-RADS US 4/5, which fails to support precise risk stratification from malignant ovarian tumors. Due to the rarity of SO, relevant studies are mostly limited to case reports or small retrospective series, lacking evidence-based differentiation models. In this study, we specifically focused on O-RADS US 4/5 SO and used the most prevalent ovarian malignancy as a comparator to develop and validate a static and dynamic individualized prediction model. This model showed excellent discrimination and clinical utility for preoperative risk stratification, providing a methodological reference for future research on differentiating SO from other ovarian tumor types. Importantly, we discovered three specific ultrasound signs of SO that are consistent with independent ultrasound predictors in the model. Furthermore, by elucidating the morphological pattern of the solid component and the " pearl sign" in SO, we have enhanced the statistical credibility and clinical usability of the model. The predictors in our model are grounded in definitive clinicopathological evidence and reflect the distinct histological features of SO and the aggressive biological behavior of HGSC. The key predictor is the extracystic location and hyperechoic appearance of the solid component. Pathologically, the solid component of SO corresponds to thyroid tissue, composed of densely packed thyroid follicles and fibrous stroma. The cystic areas on ultrasonography often correspond to colloid-filled dilated follicles or cystic degeneration. Therefore, the solid components typically distribute along the periphery of locules and present as ring-like or septal-like focal thickening. Given that abundant follicular interfaces act as acoustic scatterers, the solid component typically appears iso- to hyperechoic on ultrasonography [ 4 , 25 , 29 ]. In contrast, HGSC is characterized by invasive proliferation of highly atypical malignant epithelial cells, often forming papillary projections or irregular nodules. It is also prone to necrosis and hemorrhage, leading to heterogeneous hypoechoic or mixed echogenicity appearances [ 27 , 28 , 32 ]. Secondly, heterogeneous cyst fluid and comet-tail artifacts directly reflect colloid-rich locules in SO [39] . The locules of SO are essentially thyroid follicles filled with colloid of varying concentrations. Variation in colloid concentration causes heterogeneous intralocular echogenicity. When colloid becomes highly concentrated and develops granular or crystalline contents, punctate echogenic foci may be observed. The acoustic interface between liquid colloid and condensed particulate material can generate comet-tail artifact [ 29 , 33 , 34 ]. Although HGSC may also show heterogeneous cystic echogenicity due to hemorrhage or necrosis, these findings are generally nonspecific and typically lack these specific colloid-related features. CA125, a broad biomarker for ovarian cancer, is more frequently elevated in HGSC, but may remain normal in early-stage cases [ 35 ]. Nevertheless, CA125 may also be elevated in SO complicated by pseudo-Meigs syndrome with ascites [ 36 , 37 ]. The three characteristic ultrasonic signs of SO identified in this study—ovary-like sign, palette sign, and cheerios sign—collectively form a specific morphologic signature. They consistently show a peripheral, iso- to hyperechoic solid component surrounding the locules, forming a stable scaffold-like or encircling architecture, with multiple cystic spaces exhibiting heterogeneous internal echogenicity. These patterns offer radiologists intuitive and reproducible diagnostic criteria that align with the histologic basis of SO and may improve diagnostic consistency and accuracy. The clinical significance is reflected in three aspects. Firstly, the dynamic nomogram enhances the operability of the abstract statistical model by providing an intuitive graphical tool. This advancement improves preoperative diagnostic efficiency and facilitates future clinical promotion and multicenter validation. Secondly, the consistency between the three characteristic ultrasound signs and the independent predictors from multivariate analysis validates the credibility of the diagnostic evidence. Thirdly, by reinterpreting the spatial architecture and the “pearl sign” based on the pathophysiology, this study clarified the morphological pattern of SO and distinguished the “pearl sign” derived from thyroid tissue from the “pseudopearl sign” caused by intracystic colloid deposition, providing crucial evidence for reducing misdiagnosis. The model offers unambiguous guidance for clinical management. For patients suspected of SO, particularly those with fertility desires or during pregnancy, this can reinforce confidence in follow-up observation and avoid unnecessary laparotomy. Thus, this tool facilitates customized surgical strategies and provides a framework for precise preoperative risk stratification and management. Nevertheless, several limitations should be acknowledged. Double reading and expert confirmation improved the consistency, but retrospective design may not eliminate subjective bias. Single-center dataset without external validation also limited generalizability. Future multicenter prospective studies are needed to further verify robustness and facilitate broader clinical adoption. Moreover, the model currently distinguishes only SO from HGSC, subsequent studies must include other malignant epithelial malignancy to enhance clinical utility. 5. Conclusions We developed and validated the first static and dynamic nomogram for differentiate O-RADS US 4/-5 SO from early-stage HGSC, providing a visualized decision support tool for a major preoperative diagnostic challenge. From a pathophysiological perspective, we elucidated the key morphological patterns of SO, systematically summarized three SO-specific ultrasonographic signs, and reinterpreted the “pearl sign”. In short, these findings may support more accurate, strategic preoperative decision-making. Abbreviations SO Struma ovarii US Ultrasound O-RADS Ovarian-Adnexal Reporting and Data System MRI Magnetic resonance imaging HGSC High-grade serous ovarian carcinoma FIGO International Federation of Gynecology and Obstetrics ROC Receiver operating characteristic AUC Area under the curve DCA Decision curve analysis CI Confidence interval BSO Bilateral salpingo-oophorectomy IOTA International Ovarian Tumor Analysis Declarations Ethics approval and consent to participate This retrospective study followed the Declaration of Helsinki and was approved by both the Institutional Ethics Committee (approval No. JS-3232) and the Institutional Review Board (approval No. I-24PJ0809) of Peking Union Medical College Hospital. All methods were performed in accordance with relevant guidelines and regulations. The requirement for written informed consent was waived due to the retrospective nature of the study. Consent for publication Consent for publication was obtained from all individual participants whose data are included in this study. Availability of data and material The datasets used and/or analysed during the current study are available from thecorresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Key R&D Program of China (2023YFC2411705), the National Natural Science Foundation of China (U22A2023, 62325112, and 82402293), the PUMCH Talent Development Support Program (ULJ04684), and the National High-Level Hospital Clinical Research Funding (2022-PUMCH-D-002). Authors' contributions All authors contributed significantly to this work. Wanting Chen and Fei Ji contributed equally to this work and share first authorship. They were involved in study conception, data collection, data analysis, and manuscript drafting. Weihan Xiao and Jiajia Jiajia participated in data collection, data sorting, and result interpretation. Wenjing Zhao was responsible for scientific illustration and chart drawing. Jinghui Liang contributed to data collection and literature collection. Na Su and Meng Yang are the corresponding authors, responsible for study design, supervision, critical revision of the manuscript, and final approval of the version to be published. All authors read and approved the final manuscript. Acknowledgements Not applicable References Willemse PH, Oosterhuis JW, Aalders JG, et al. Malignant struma ovarii treated by ovariectomy, thyroidectomy, and 131I administration. Cancer. 1987;60(2):178–82. https://doi.org/10.1002/1097-0142(19870715)60:2%3C178::aid-cncr2820600210%3E3.0.co;2-q . Makani S, Kim W, Gaba AR. Struma Ovarii with a focus of papillary thyroid cancer: a case report and review of the literature. Gynecol Oncol. 2004;94(3):835–9. https://doi.org/10.1016/j.ygyno.2004.06.003 . Fujiwara S, Tsuyoshi H, Nishimura T, Takahashi N, Yoshida Y. Precise preoperative diagnosis of struma ovarii with pseudo-Meigs' syndrome mimicking ovarian cancer with the combination of (131)I scintigraphy and (18)F-FDG PET: case report and review of the literature. J Ovarian Res. 2018;11(1):11. https://doi.org/10.1186/s13048-018-0383-2 . Tondi Resta I, Sande CM, LiVolsi VA. Neoplasms in Struma Ovarii: A Review. Endocr Pathol. 2023;34(4):455–60. https://doi.org/10.1007/s12022-023-09789-7 . Yoo SC, Chang KH, Lyu MO, Chang SJ, Ryu HS, Kim HS. Clinical characteristics of struma ovarii. J Gynecol Oncol. 2008;19(2):135–8. https://doi.org/10.3802/jgo.2008.19.2.135 . Egan C, Stefanova D, Thiesmeyer JW, et al. Proposed Risk Stratification and Patterns of Radioactive Iodine Therapy in Malignant Struma Ovarii. Thyroid. 2022;32(9):1101–8. https://doi.org/10.1089/thy.2022.0145 . Goffredo P, Sawka AM, Pura J, Adam MA, Roman SA, Sosa JA. Malignant struma ovarii: a population-level analysis of a large series of 68 patients. Thyroid. 2015;25(2):211–5. https://doi.org/10.1089/thy.2014.0328 . Gobitti C, Sindoni A, Bampo C, et al. Malignant struma ovarii harboring a unique NRAS mutation: case report and review of the literature. Horm (Athens). 2017;16(3):322–7. https://doi.org/10.14310/horm.2002.1750 . Kempers RD, Dockerty MB, Hoffman DL, Bartholomew LG. Struma ovarii–ascitic, hyperthyroid, and asymptomatic syndromes. Ann Intern Med. 1970;72(6):883–93. https://doi.org/10.7326/0003-4819-72-6-883 . Li S, Yang T, Xiang Y, Li X, Zhang L, Deng S. Clinical characteristics and survival outcomes of malignant struma ovarii confined to the ovary. BMC Cancer. 2021;21(1):383. https://doi.org/10.1186/s12885-021-08118-7 . Tamura N, Murakami K, Ozaki R, et al. Current state of management of struma ovarii and preoperative imaging features: A retrospective case series study of 18 patients at a single institution. J Obstet Gynaecol Res. 2023;49(3):1007–11. https://doi.org/10.1111/jog.15545 . Strachowski LM, Jha P, Phillips CH, et al. Radiology. 2023;308(3):e230685. https://doi.org/10.1148/radiol.230685 . O-RADS US v2022: An Update from the American College of Radiology's Ovarian-Adnexal Reporting and Data System US Committee. Savelli L, Testa AC, Timmerman D, Paladini D, Ljungberg O, Valentin L. Imaging of gynecological disease (4): clinical and ultrasound characteristics of struma ovarii. Ultrasound Obstet Gynecol. 2008;32(2):210–9. https://doi.org/10.1002/uog.5396 . Weinberger V, Kadlecova J, Minář L, et al. Struma ovarii - ultrasound features of a rare tumor mimicking ovarian cancer. Med Ultrason. 2018;20(3):355–61. https://doi.org/10.11152/mu-1526 . Chen M, Liao S, Xu Y, Ye X, Jia X, Zhang S. Clinicopathological and imaging features of struma ovarii: a retrospective study. Front Oncol. 2025;15:1487812. https://doi.org/10.3389/fonc.2025.1487812 . Matsuki M, Kaji Y, Matsuo M, Kobashi Y. Struma ovarii: MRI findings. Br J Radiol. 2000;73(865):87–90. https://doi.org/10.1259/bjr.73.865.10721328 . Ye R, Zheng Y, Pan F, Wang H, Yan C, Li Y. Differentiating struma ovarii from FIGO stage I malignant ovarian tumors in O-RADS MRI 5 lesions: a targeted cohort study. Abdom Radiol (NY). 2025;50(3):1426–34. https://doi.org/10.1007/s00261-024-04564-6 . Ishiguro T, Saida T, Shikama A, et al. Diagnostic imaging analysis to differentiate struma ovarii from mucinous carcinomas, encompassing T2*-based imaging, diffusion-weighted imaging, and dynamic contrast-enhanced imaging. Br J Radiol. 2024;97(1163):1843–9. https://doi.org/10.1093/bjr/tqae165 . Caruso G, Weroha SJ, Cliby W. Ovarian Cancer: A Review. JAMA. 2025;334(14):1278–91. https://doi.org/10.1001/jama.2025.9495 . Eisenhauer EA. Real-world evidence in the treatment of ovarian cancer. Ann Oncol. 2017;28(suppl8):viii61–5. https://doi.org/10.1093/annonc/mdx443 . Kraemer B, Grischke EM, Staebler A, Hirides P, Rothmund R. Laparoscopic excision of malignant struma ovarii and 1 year follow-up without further treatment. Fertil Steril. 2011;95(6):2124. https://doi.org/10.1016/j.fertnstert.2010.12.047 . .e9-12. Llueca A, Maazouzi Y, Herraiz JL, et al. Treatment and follow-up in an asymptomatic malignant struma ovarii: A case report. Int J Surg Case Rep. 2017;40:113–5. https://doi.org/10.1016/j.ijscr.2017.09.005 . Hassan SA, Akhtar A, Falah NU, Sheikh FN. Malignant Thyroid-type Papillary Neoplasm in Struma Ovarii: A Case Report. Cureus. 2019;11(12):e6450. https://doi.org/10.7759/cureus.6450 . Wei S, Baloch ZW, LiVolsi VA. Pathology of Struma Ovarii: A Report of 96 Cases. Endocr Pathol. 2015;26(4):342–8. https://doi.org/10.1007/s12022-015-9396-1 . Fitzpatrick M, Olkhov-Mitsel E, Amemiya Y, et al. Clinicopathologic, Immunohistochemical, and Molecular Analysis of Primary Ovarian Carcinoid Tumors With Correlation of Ki67 Proliferation Index With Patient Outcomes. Mod Pathol. 2025;38(11):100822. https://doi.org/10.1016/j.modpat.2025.100822 . Taylor EC, Irshaid L, Mathur M. Multimodality Imaging Approach to Ovarian Neoplasms with Pathologic Correlation. Radiographics. 2021;41(1):289–315. https://doi.org/10.1148/rg.2021200086 . Hassen K, Ghossain MA, Rousset P, et al. Characterization of papillary projections in benign versus borderline and malignant ovarian masses on conventional and color Doppler ultrasound. AJR Am J Roentgenol. 2011;196(6):1444–9. https://doi.org/10.2214/ajr.10.5014 . Moro F, Baima Poma C, Zannoni GF, et al. Imaging in gynecological disease (12): clinical and ultrasound features of invasive and non-invasive malignant serous ovarian tumors. Ultrasound Obstet Gynecol. 2017;50(6):788–99. https://doi.org/10.1002/uog.17414 . Feng Y, Chen Y, Wu Q, Bao Z, Ning C, Zhao C. A retrospective case series at a tertiary hospital in china: ultrasonographic features of Struma ovarii. BMC Cancer. 2025;25(1):1180. https://doi.org/10.1186/s12885-025-14606-x . Zannoni L, Savelli L, Jokubkiene L, et al. Intra- and interobserver agreement with regard to describing adnexal masses using International Ovarian Tumor Analysis terminology: reproducibility study involving seven observers. Ultrasound Obstet Gynecol. 2014;44(1):100–8. https://doi.org/10.1002/uog.13273 . Timmerman D, Valentin L, Bourne TH, Collins WP, Verrelst H, Vergote I. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) Group. Ultrasound Obstet Gynecol. 2000;16(5):500–5. https://doi.org/10.1046/j.1469-0705.2000.00287.x . Vang R, Shih Ie M, Kurman RJ. Ovarian low-grade and high-grade serous carcinoma: pathogenesis, clinicopathologic and molecular biologic features, and diagnostic problems. Adv Anat Pathol. 2009;16(5):267–82. https://doi.org/10.1097/PAP.0b013e3181b4fffa . Malhi H, Beland MD, Cen SY, et al. Echogenic foci in thyroid nodules: significance of posterior acoustic artifacts. AJR Am J Roentgenol. 2014;203(6):1310–6. https://doi.org/10.2214/ajr.13.11934 . Wu H, Zhang B, Li J, Liu Q, Zhao T. Echogenic foci with comet-tail artifact in resected thyroid nodules: Not an absolute predictor of benign disease. PLoS ONE. 2018;13(1):e0191505. https://doi.org/10.1371/journal.pone.0191505 . Kaaks R, Cooley V, Mukama T, et al. A Prospective Study Consortium for the Discovery and Validation of Early Detection Markers for Ovarian Cancer - Baseline Findings for CA125. Clin Cancer Res. 2025;31(12):2441–53. https://doi.org/10.1158/1078-0432.Ccr-24-1845 . Li S, Hong R, Yin M, Zhang X, Zhang T, Yang J. Struma ovarii with synchronous ascites and elevated CA125 level: a retrospective cohort study. Acta Oncol. 2023;62(8):889–96. https://doi.org/10.1080/0284186x.2023.2226798 . Wang S, He X, Yang H, Chen L. Struma Ovarii Associated with Ascites and Elevated CA125: Two Case Reports and Review of the Literature. Int J Womens Health. 2022;14:1291–6. https://doi.org/10.2147/ijwh.S379128 . Additional Declarations No competing interests reported. Supplementary Files Supplement.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 19 Apr, 2026 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 Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9459948","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637375826,"identity":"dcebebcc-5b1d-4ebd-bf11-baa91f87d07e","order_by":0,"name":"Wanting Chen","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Wanting","middleName":"","lastName":"Chen","suffix":""},{"id":637375827,"identity":"4db6b7f4-48cf-4901-9c59-978af4db633e","order_by":1,"name":"Fei Ji","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Ji","suffix":""},{"id":637375828,"identity":"494ea7f4-0bdd-4622-8680-7bc0dbc814c0","order_by":2,"name":"Weihan Xiao","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Weihan","middleName":"","lastName":"Xiao","suffix":""},{"id":637375829,"identity":"57ca476b-1449-4913-be63-92e16f38de64","order_by":3,"name":"Jiajia Tang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jiajia","middleName":"","lastName":"Tang","suffix":""},{"id":637375830,"identity":"e6d1c05b-6ecc-47ad-b9e9-034c24424aee","order_by":4,"name":"Wenjing Zhao","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Zhao","suffix":""},{"id":637375831,"identity":"b581baf3-3848-4e55-bee0-208f0ee987e6","order_by":5,"name":"Jinghui Liang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jinghui","middleName":"","lastName":"Liang","suffix":""},{"id":637375832,"identity":"424bde19-cacf-434f-8e5c-8724d497cf8e","order_by":6,"name":"Na Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBADGYbjjY0PPpCihYfhzOFmwxmkabmR3ibNQYxS+Rm5Bz/zVNzj4bv5sEGagcFOTreBgBaDG3nJ0jxninkkbyc2GBcwJBubHSCkRSLHQDq3LYHHAKgleQbDgcRthLTIz8gx/g3WcvNgw2EeYrQw3Mgxg9hyg7GxmSgtBmfemFn/OZPAI3kmsZlxhgERfpFvzzG+OaMiQY7v+PHnPz5U2MkR1IJuKWnKR8EoGAWjYBTgAADILUMubJ5oOAAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Na","middleName":"","lastName":"Su","suffix":""},{"id":637375833,"identity":"2a992bfb-ec9c-4160-be74-91189ca72603","order_by":7,"name":"Meng Yang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-04-19 07:08:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9459948/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9459948/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109295981,"identity":"c49d8161-6ea0-4afa-acc9-a06bd4d1e63d","added_by":"auto","created_at":"2026-05-15 08:42:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":743592,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for preoperative discrimination between struma ovarii (SO) and high‑grade serous carcinoma (HGSC). The nomogram was constructed from multivariate logistic regression analysis (A). To determine the risk of SO, points are assigned for each variable according to the position along the ‘points’ scale, summed, and the corresponding probability is identified on the total points scale. Receiver operating characteristic (ROC) curves (B), decision curve analysis (DCA) (C), and calibration curves in the training (D) and calibration curve in the test set (E).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/fe3b010337b54bb00d324021.jpg"},{"id":109252418,"identity":"2fc4a20a-b4f4-4bdc-a263-aa6469b2bb8a","added_by":"auto","created_at":"2026-05-14 09:26:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1008961,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative application of the individualized prediction nomogram for SO (a,b) and HGSC (c,d). The probability of SO was calculated based on the five selected predictor variables.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/a02fbe879aa62a1b30f35dee.jpg"},{"id":109222319,"identity":"edda6dd1-06f4-41d9-8e4d-814cd674f953","added_by":"auto","created_at":"2026-05-13 21:07:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27709986,"visible":true,"origin":"","legend":"\u003cp\u003eGrayscale ultrasound and three-dimensional (3D) rendering of SO and HGSC. 3D ultrasound more clearly demonstrates the solid components located at the periphery of locules, which present as ring-like (asterisks in a, b) or septal-like focal thickening (arrows in c, d) in SO. The arrow in (e,f) points to a continuous cyst wall in SO, and the asterisk marks the extracystic solid component. The arrows in (g,h) point to solid papillary projections in HGSC.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/3fca06ce2cf8bef3812b9aff.png"},{"id":109222295,"identity":"dd2affa6-0ee3-4dda-9756-7cf36500bb23","added_by":"auto","created_at":"2026-05-13 21:07:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":195900,"visible":true,"origin":"","legend":"\u003cp\u003eGrayscale ultrasound images (a, e), corresponding microvascular images (b, f), macroscopic appearance (c, g), and histopathology (d, f), and for SO (a-d) and HGSC (e-h).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/f85dd7f894404eba9abe9d71.jpg"},{"id":109222325,"identity":"150f16c3-a8cf-4f7c-8f78-cb9110880eb6","added_by":"auto","created_at":"2026-05-13 21:07:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":236503,"visible":true,"origin":"","legend":"\u003cp\u003eTypical ultrasound signs and schematic illustrations of SO. (a) Ovary-like sign. (b) \u0026nbsp;palette sign. The arrows in (b) points to solid core. (c) Cheerios sign. The arrows in (c) points to hyperechoic ring.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/104e9a068c44b22b7ab2d112.jpg"},{"id":109252413,"identity":"a26286bd-650f-4f68-9b6e-aafa628127b2","added_by":"auto","created_at":"2026-05-14 09:26:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":26167552,"visible":true,"origin":"","legend":"\u003cp\u003ePearl sign and pseudopearl sign in SO on ultrasound. Grayscale (a) and color Doppler (b) images illustrating the “pearl sign” (arrows) from thyroid tissue in Patient 1. Grayscale (c) and color Doppler (d) images illustrating the “pseudopearl sign” (arrows) from deposits in Patient 2.\u003cstrong\u003e \u003c/strong\u003e(e–h) Sequential development of the “pseudopearl sign” from colloid within a cystic cavity of SO. The images show the dynamic formation process, including deposition (Months 0, 2), aggregation (Month 4), and compaction (Month 6).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/606d03edf955c1cb05a47fd1.png"},{"id":109297552,"identity":"feff21a0-fe7e-4b8e-a2fe-1334239a202c","added_by":"auto","created_at":"2026-05-15 08:59:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":53062185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/78707aca-310a-4bdd-8e13-744e31ba5935.pdf"},{"id":109222294,"identity":"7162684b-1c32-4eed-b7ca-e44b77c87b19","added_by":"auto","created_at":"2026-05-13 21:07:01","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":327680,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.doc","url":"https://assets-eu.researchsquare.com/files/rs-9459948/v1/d3231c9e0e38a079fa9622d4.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Individualized risk prediction nomogram and ultrasound signs for preoperative differentiation of struma ovarii","fulltext":[{"header":"1. Background","content":"\u003cp\u003eStruma ovarii (SO) is a rare, highly differentiated monodermal teratoma defined by thyroid tissue comprising more than 50% of the tumor [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], accounting for 2\u0026ndash;3% of teratomas and 0.5-1% of ovarian tumors [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although malignant transformation occurs in 5\u0026ndash;10% of cases, prognosis is generally favorable in both benign and nonmetastatic cases [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Clinical presentation is often nonspecific, asymptomatic patients may be incidentally identified during routine physical examinations or imaging screenings, which substantially increases the difficulty of preoperative diagnosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Due to its rarity, existing evidence primarily comes from case reports and small retrospective series, and standardized diagnostic and management guidelines are still lacking [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurate preoperative diagnosis is crucial for appropriate management plans. Ultrasound (US) is the first-line imaging modality for adnexal masses. The Ovarian-Adnexal Reporting and Data System (O-RADS) US offers a standardized assessment framework for clinical practice [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, SO often presents as a multilocular-solid mass with moderate to abundant blood flow signals on ultrasound,, classified as O-RADS 4/5 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. magnetic resonance imaging (MRI) may show lobulated cystic-solid lesions with a \"stained-glass\" appearance. High signal intensity on T1-weighted images or punctate low-signal foci on T2-weighted images may also be observed [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, high-risk time-intensity curve patterns without diffusion restriction [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] lead to O-RADS MRI 5 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These features overestimate malignancy risk and lack reliable discriminatory value [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eApproximately 90% of ovarian cancers are epithelial malignancies, 70% -80% of which are high-grade serous ovarian cancers (HGSC), the most common and aggressive subtype [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Early-stage (FIGO I-II) HGSC often appears as O-RADS US 4/-5 complex cystic-solid mass with abundant blood flow. Thus, HGSC was chosen as the primary comparator for SO due to its high incidence, making their differentiation clinically critical. More importantly, preoperative differentiation directly guides surgical strategy and patient benefit. Early-stage HGSC requires standardized staging surgery and systemic treatment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], whereas SO can generally be managed conservatively [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, no imaging-based prediction model specifically exists to preoperatively differentiate SO from early-stage ovarian cancer, and O-RADS US is insufficient. To fill this gap, we retrospectively analyzed O-RADS 4/5 SO lesions and FIGO stage I-II HGSC to develop and validate an individualized preoperative risk prediction nomogram and an interactive dynamic nomogram. We also propose three characteristic ultrasound signs for SO, which may serve as valuable imaging clues for differentiating SO from malignant ovarian cancers. This study has the potential to enhance the accuracy of preoperative risk stratification and provide a more reliable basis for surgical planning and clinical decision-making.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eThis retrospective study included patients with gynecologic surgery for ovarian masses at Peking Union Medical College Hospital between January 2017 and December 2025. Inclusion criteria were: (1) complete preoperative clinical data and evaluable ultrasound images; (2) O-RADS US 4/5 cystic-solid lesions pathologically confirmed as SO or FIGO stage I-II HGSC postoperatively. Exclusion criteria were: (1) preoperative treatment for ovarian tumors; (2) O-RADS US 1\u0026ndash;3 lesions; and (3) incomplete clinical data or non-evaluable ultrasound images. After screening, 93 SO lesions from 92 patients and 87 early stage HGSC lesions from 79 patients were included. This retrospective study followed the Declaration of Helsinki and received approval from the Institutional Review Board of our hospital (IRB No. I-24PJ0809).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical variables\u003c/h2\u003e \u003cp\u003eGiven their availability in routine practice and predictive value, key variables were from the electronic medical record system, including: (1) age; (2) family history (yes/no), breast/ovarian/gynecologic malignancies in first/second-degree relatives; (3) symptoms (yes/no), abdominal pain, distension, irregular vaginal bleeding, or palpable abdominal mass; and (4) CA125\u0026thinsp;\u0026gt;\u0026thinsp;35 U/mL (yes/no). All data were collated using standardized collection templates and independently verified by two investigators. Comorbidities and surgical approach were additionally recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ultrasound Examinations and Feature\u003c/h2\u003e \u003cp\u003ePreoperative ultrasound examinations were performed by certified radiologists using standardized equipment including GE Voluson E10 (GE Healthcare, USA), Philips EPIQ 7 (Philips Healthcare, the Netherlands), and Philips iU22, with transabdominal (2.5-5.0 MHz) and transvaginal (5.0\u0026ndash;9.0 MHz) probes. Each lesion was assessed in multiple planes, and at least two orthogonal grayscale and color Doppler images were stored. Lesions were selected and classified as according to the O-RADS US 2022 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Based on expert consensus derived from extensive clinical experience, the following ultrasound variables were analyzed: (1) laterality (unilateral/bilateral); (2) largest diameter in any plane; (3) contour (regular/irregular); (4) number of locules (1, 2\u0026ndash;4 or \u0026ge;\u0026thinsp;5); (5) regularity of locules (yes/no); (6) homogeneity of cyst fluid (yes/ no); (7) comet_tail artifact (yes/no); (8) morphological pattern of the solid component (intracystic solid component, extracystic solid tissue or predominantly solid component). Intracystic solid component denotes a papillary projection or nodule (height\u0026thinsp;\u0026ge;\u0026thinsp;3 mm) from the cyst wall or septation protruding into the cyst cavity. Extracystic solid tissue refers to solid components distributed along the periphery of locules, presenting as ring-like or septal-like focal thickening. Predominantly solid component refers to a lesion composed of at least 80% solid tissue, with minor cystic components. (9) echogenicity of solid component (hypoechoic, isoechoic, or hyperechoic), relative to the myometrium; (10) color score (grade 0\u0026ndash;1, 2 or 3) (11) calcification (yes/no); and (12) free intraperitoneal fluid (yes/no). All images were independently reviewed by two radiologists with over 10 years of experience in gynecological imaging. Discrepancies were confirmed by a senior expert with more than 20 years of clinical experience. All images were de -identified and randomly anonymized before analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R (version 4.3.1) and SPSS (version 26.0). The cohort was randomly allocated into a training set (n\u0026thinsp;=\u0026thinsp;126) and a validation set (n\u0026thinsp;=\u0026thinsp;54) at a 7:3 ratio. Continuous variables were expressed as median (interquartile range) and group comparisons using the t test or Mann-Whitney U-test. Categorical variables were expressed as n (%) and compared between groups using the chi-square test or Fisher\u0026rsquo;s exact test. Univariate and multivariate logistic regression identified independent predictors to develop a clinical-ultrasound nomogram for individualized risk prediction. Furthermore, a dynamic nomogram was constructed using the \"DynNom\" and \"shiny\" packages in R. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) were used to evaluate the predictive performance and clinical utility of the model. Statistical tests were two-tailed and significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline clinical and ultrasonographic characteristics of the training and validation cohorts, with no statistically significant differences observed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Detailed clinical and ultrasonographic characteristics and ultrasonographic features of SO patients and lesions are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively. Among SO cases, 65 patients (70.7%) were asymptomatic and incidentally detected during physical examinations or imaging studies. Notably, 3 patients (3.3%) were diagnosed during pregnancy. Laboratory findings revealed elevated CA125 levels (\u0026gt;\u0026thinsp;35 U/mL) in 23 patients (25.0%) and thyroid dysfunction in 12 patients (13.0%). Ascites was present in 7 patients (7.6%), fluid outside the pouch of Douglas. All assessed lesions were cystic-solid masses. 13 lesions (14.0%) were classified as O-RADS 4, and 80 lesions (86.0%) as O-RADS 5. In the initial ultrasonographic reports, 25 lesions (26.9%) were misclassified as malignant, and 37 lesions (39.8%) could not be definitively characterized. Postoperative pathological analysis revealed 69 cases (75.0%) of pure SO, 20 cases (21.7%) of SO combined with dermoid cyst, and 3 cases (3.3%) of SO combined with cystadenoma. Among these, 4 cases (4.3%) were malignant SO, 2 cases (2.2%) were carcinoid tumors, and 2 cases (2.2%) were microfocal papillary carcinoma, with no distant or local metastasis observed in any case. Regarding surgical management, 52 of 92 SO patients (56.5%) were premenopausal. Among these premenopausal patients, 9 (17.3%) underwent hysterectomy with bilateral salpingo-oophorectomy (BSO), including 3 who underwent open hysterectomy with BSO, which suggested potential overtreatment in some cases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics in the training and test cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining (N\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTesting (N\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.00 (44.00\u0026ndash;62.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.50 (44.00-59.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest_diameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.10 (5.70\u0026ndash;10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.75 (5.62\u0026ndash;9.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily_history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 (86.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptom\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eO-RADS US\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 (86.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUltrasound_assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaterality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (92.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are given as n (%) or median (interquartile range). ORADS, the Ovarian-Adnexal Reporting and Data System.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of women with SO (N\u0026thinsp;=\u0026thinsp;92).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.50(38.50, 61.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenopausal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbdominal_pain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (83.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbdominal_distension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (80.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIrregular_bleeding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbdominal_mass\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAscites\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85(92.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7(7.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA125\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 35 U/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 35 U/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThyroid_dysfunction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (87.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConcomitant_disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDermoid cyst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystadenoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical_approach\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic conservative surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic hysterectomy with BSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen hysterectomy with BSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are given as n (%) or median (interquartile range). Clinical and laboratory variables were collected at the time of SO diagnosis. BSO: bilateral salpingo-oophorectomy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUltrasonic features of the SO lesions (N\u0026thinsp;=\u0026thinsp;93).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest_diameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.40(4.90, 9.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eO-RADS US\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (86.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubjective ultrasound assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are given as n (%) or median (interquartile range).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Clinical and ultrasound predictors for two diseases\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a comparative clinical indicators and ultrasound parameters of SO and early-stage HGSC in the training set. There were significant differences between the two groups for several variables, including CA125 (p\u0026thinsp;=\u0026thinsp;0.001), heterogeneity of cyst fluid (p\u0026thinsp;=\u0026thinsp;0.025), comet_tail artifact (p\u0026thinsp;=\u0026thinsp;0.036), morphological pattern of the solid component (p\u0026thinsp;=\u0026thinsp;0.03), and echogenicity of solid components (p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses of clinical and ultrasound indicators of SO and HGSC in training set.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHGSC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily_history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (90.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptom\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA125\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026ndash;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 35 U/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 35 U/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (83.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest_diameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaterality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01-1308036.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (80.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVascularity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Minimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eirregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocular number\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCystic regularity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCystic echogenicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026ndash;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheterogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehomogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComet-tail artifact\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13\u0026ndash;43.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (88.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSolid pattern\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracystic solid component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtracystic solid component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53-118.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredominantly solid mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u0026ndash;6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSolid echogenicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoechoic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoechoic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026ndash;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperechoic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026ndash;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalcification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (85.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (82.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFree intraperitoneal fluid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are given as n (%) or median (interquartile range). CI, confidence interval; OR, odds ratio.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance for differentiating SO from early-stage HGSC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTraining\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCohort\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSEN (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPE (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003cp\u003e(0.949\u0026ndash;0.994)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.919\u0026ndash;0.921)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003cp\u003e(0.900-1.000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003cp\u003e(0.808\u0026ndash;0.966)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003cp\u003e(0.822\u0026ndash;0.969)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003cp\u003e(0.891-1.000)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTesting\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003cp\u003e(0.882\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003cp\u003e(0.869\u0026ndash;0.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003cp\u003e(0.657\u0026ndash;0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003cp\u003e(0.883-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003cp\u003e(0.883-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003cp\u003e(0.657\u0026ndash;0.943)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eACC\u003c/b\u003e, accuracy; \u003cb\u003eAUC\u003c/b\u003e, area under the Curve; \u003cb\u003eACC\u003c/b\u003e, accuracy; \u003cb\u003eSEN\u003c/b\u003e, sensitivity; \u003cb\u003eSPE\u003c/b\u003e, specificity; \u003cb\u003eNPV\u003c/b\u003e, negative predictive value; \u003cb\u003ePPV\u003c/b\u003e, positive predictive value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Model performance and validation\u003c/h2\u003e \u003cp\u003eUsing independent predictors derived from multivariable regression, a combined clinical-ultrasound nomogram was established to predict the probability of SO and early-stage HGSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the AUC was 0.971 (95%CI, 0.949\u0026ndash;0.994) in the training cohort and 0.939 (95%CI, 0.882\u0026ndash;0.995) in the testing cohort, with corresponding accuracy values of 0.920 (95%CI, 0.919\u0026ndash;0.921) and 0.873 (95%CI, 0.869\u0026ndash;0.877), respectively, indicating excellent discriminative ability between two diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Calibration curves demonstrated great agreement between predicted and observed probabilities in both cohorts, confirming satisfactory calibration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). DCA showed favorable net clinical benefit across a wide range of threshold probabilities, validating its clinical utility for differentiating SO from early-stage HGSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003eDiagnostic performance for differentiating SO from early-stage HGSC.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTraining\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCohort\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSEN (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPE (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003cp\u003e(0.949\u0026ndash;0.994)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.919\u0026ndash;0.921)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003cp\u003e(0.900-1.000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003cp\u003e(0.808\u0026ndash;0.966)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003cp\u003e(0.822\u0026ndash;0.969)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003cp\u003e(0.891-1.000)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTesting\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003cp\u003e(0.882\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003cp\u003e(0.869\u0026ndash;0.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003cp\u003e(0.657\u0026ndash;0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003cp\u003e(0.883-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003cp\u003e(0.883-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003cp\u003e(0.657\u0026ndash;0.943)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eACC\u003c/b\u003e, accuracy; \u003cb\u003eAUC\u003c/b\u003e, area under the Curve; \u003cb\u003eACC\u003c/b\u003e, accuracy; \u003cb\u003eSEN\u003c/b\u003e, sensitivity; \u003cb\u003eSPE\u003c/b\u003e, specificity; \u003cb\u003eNPV\u003c/b\u003e, negative predictive value; \u003cb\u003ePPV\u003c/b\u003e, positive predictive value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Utilization of individual prediction nomogram\u003c/h2\u003e \u003cp\u003eTo illustrate the clinical application workflow of the individualized risk prediction nomogram, this study enrolled four newly diagnosed cases of SO and early-stage HGSC respectively. We extracted their clinical and ultrasound variables and summed the scores corresponding to each predictive variable in the nomogram. Finally, the probability of SO and early-stage HGSC for each patient was calculated. The pathological diagnosis of SO in cases with higher predicted probabilities supports the accuracy of the model. This nomogram allows for an intuitive, quantifiable risk assessment and serves as a reference for preoperative risk stratification and clinical decision-making (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Dynamic nomogram for Predicting SO\u003c/h2\u003e \u003cp\u003eBuilding on the traditional risk prediction nomogram for distinguishing SO from early-stage HGSC, we created a dynamic online nomogram to enable more efficient and convenient individualized risk assessment. The website is: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sopredict.shinyapps.io/Ovarian_Nomogram_App/(\u003c/span\u003e\u003cspan address=\"https://sopredict.shinyapps.io/Ovarian_Nomogram_App/(\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The pathological basis of the morphological pattern of the solid component\u003c/h2\u003e \u003cp\u003eThe morphological pattern of the solid component differs significantly between SO and HGSC. In SO, the solid component is located along the cystic cavities. Although some larger cystic cavities may cause the intervening solid tissue to appear to protrude into the cavity, which can be mistakenly identified as papillary projections, three-dimensional (3D) ultrasound can more intuitively clarify their spatial relationship and highlight the hyperechoic features of the solid components (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-d). Careful observation usually reveals a continuous and smooth cyst wall between the solid components and the cyst cavity, which contrasts with the genuine papillary projections or mural nodules in HGSC, where the intracystic excrescences disrupt the smooth inner wall (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-h). In addition, MRI also supported this observation. The signal within SO cystic cavities can present a \"stained glass appearance\" due to variations in colloid viscosity and concentration, and cyst wall or septa may show enhancement, but no obvious enhancement of solid components within the cystic cavities has been described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePathologically, these imaging differences reflect distinct tissue origins. SO presents as a solid mass with scattered round or oval cystic cavities lined by smooth walls and filled with abundant colloid-like material. The solid components of SO correspond to thyroid tissue consisting of follicular epithelium and vascular-rich fibrous stroma. These cystic cavities tend to originate from thyroid follicles or cystic degeneration, lack solid components [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, HGSC commonly appears as grayish-yellow solid mass with hemorrhage, necrosis, and irregular cystic cavities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It is characterized by high-grade malignant epithelial proliferation with fibrovascular cores, and papillae and mural nodules may project into cystic spaces and show invasive growth [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Characteristic ultrasonic signs of SO\u003c/h2\u003e \u003cp\u003eTo enhance model interpretability, we summarized three characteristic ultrasound signs of SO, all of which align with the ultrasound variables selected by the model. They share the following common characteristics: the isoechoic to hyperechoic solid components are distributed peripherally or between cystic cavities without intracystic protrusion, forming stable peripheral scaffold-like or ring-liked structures. The cyst contents are heterogeneous and occasional show comet-tail artifact. The three specific ultrasonographic features include: (1) \u003cem\u003eOvary-like sign\u003c/em\u003e: Multiple round or oval cystic locules within the solid component form an ovary-like appearance; (2) \u003cem\u003epalette sign\u003c/em\u003e: a well-defined solid core surrounded by multiple cysts containing heterogeneous contents, creating a petal configuration; (3) \u003cem\u003eCheerios sign\u003c/em\u003e: A well-circumscribed, hyperechoic solid area with a central cyst within a multilocular-solid mass. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Reinterpretation of the Pearl Sign\u003c/h2\u003e \u003cp\u003ePrevious studies have suggested that the \"pearl sign\" is a specific ultrasonographic feature of SO, corresponding to the \"petal sign\" proposed above. It is described as a smooth roundish solid area with vascularization [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our cases, the hyperechoic solid components in SO are typically peripheral to cystic cavities with moderate to intense vascularity, as they derive from thyroid tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b). Notably, the \"pseudopearl sign\" from deposits arises from the gradual deposition and compaction of intracystic colloid over time, appearing as hypoechoic or hyperechoic flocculent aggregates and may be misinterpreted as the \"pearl sign\"(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee-h) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Unlike true thyroid tissue, these colloid deposits may show slight deformation or displacement with postural changes or pressure from the operator and generally show no internal blood flow signals on color Doppler ultrasound(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). The IOTA group categorized such deposits as a pitfall in distinguishing intracystic solid components and recommended, Doppler and dynamic scanning to reduce errors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Distinguishing these signs is critical to minimize misdiagnosis and validate the \"pearl sign\" specificity for SO. Only excluding colloid deposition can reliably assess its true value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCurrently, cystic-solid SO lesions are frequently classified as O-RADS US 4/5, which fails to support precise risk stratification from malignant ovarian tumors. Due to the rarity of SO, relevant studies are mostly limited to case reports or small retrospective series, lacking evidence-based differentiation models. In this study, we specifically focused on O-RADS US 4/5 SO and used the most prevalent ovarian malignancy as a comparator to develop and validate a static and dynamic individualized prediction model. This model showed excellent discrimination and clinical utility for preoperative risk stratification, providing a methodological reference for future research on differentiating SO from other ovarian tumor types. Importantly, we discovered three specific ultrasound signs of SO that are consistent with independent ultrasound predictors in the model. Furthermore, by elucidating the morphological pattern of the solid component and the \" pearl sign\" in SO, we have enhanced the statistical credibility and clinical usability of the model.\u003c/p\u003e \u003cp\u003eThe predictors in our model are grounded in definitive clinicopathological evidence and reflect the distinct histological features of SO and the aggressive biological behavior of HGSC. The key predictor is the extracystic location and hyperechoic appearance of the solid component. Pathologically, the solid component of SO corresponds to thyroid tissue, composed of densely packed thyroid follicles and fibrous stroma. The cystic areas on ultrasonography often correspond to colloid-filled dilated follicles or cystic degeneration. Therefore, the solid components typically distribute along the periphery of locules and present as ring-like or septal-like focal thickening. Given that abundant follicular interfaces act as acoustic scatterers, the solid component typically appears iso- to hyperechoic on ultrasonography [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In contrast, HGSC is characterized by invasive proliferation of highly atypical malignant epithelial cells, often forming papillary projections or irregular nodules. It is also prone to necrosis and hemorrhage, leading to heterogeneous hypoechoic or mixed echogenicity appearances [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Secondly, heterogeneous cyst fluid and comet-tail artifacts directly reflect colloid-rich locules in SO \u003csup\u003e[39]\u003c/sup\u003e. The locules of SO are essentially thyroid follicles filled with colloid of varying concentrations. Variation in colloid concentration causes heterogeneous intralocular echogenicity. When colloid becomes highly concentrated and develops granular or crystalline contents, punctate echogenic foci may be observed. The acoustic interface between liquid colloid and condensed particulate material can generate comet-tail artifact [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although HGSC may also show heterogeneous cystic echogenicity due to hemorrhage or necrosis, these findings are generally nonspecific and typically lack these specific colloid-related features. CA125, a broad biomarker for ovarian cancer, is more frequently elevated in HGSC, but may remain normal in early-stage cases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Nevertheless, CA125 may also be elevated in SO complicated by pseudo-Meigs syndrome with ascites [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe three characteristic ultrasonic signs of SO identified in this study\u0026mdash;ovary-like sign, palette sign, and cheerios sign\u0026mdash;collectively form a specific morphologic signature. They consistently show a peripheral, iso- to hyperechoic solid component surrounding the locules, forming a stable scaffold-like or encircling architecture, with multiple cystic spaces exhibiting heterogeneous internal echogenicity. These patterns offer radiologists intuitive and reproducible diagnostic criteria that align with the histologic basis of SO and may improve diagnostic consistency and accuracy.\u003c/p\u003e \u003cp\u003eThe clinical significance is reflected in three aspects. Firstly, the dynamic nomogram enhances the operability of the abstract statistical model by providing an intuitive graphical tool. This advancement improves preoperative diagnostic efficiency and facilitates future clinical promotion and multicenter validation. Secondly, the consistency between the three characteristic ultrasound signs and the independent predictors from multivariate analysis validates the credibility of the diagnostic evidence. Thirdly, by reinterpreting the spatial architecture and the \u0026ldquo;pearl sign\u0026rdquo; based on the pathophysiology, this study clarified the morphological pattern of SO and distinguished the \u0026ldquo;pearl sign\u0026rdquo; derived from thyroid tissue from the \u0026ldquo;pseudopearl sign\u0026rdquo; caused by intracystic colloid deposition, providing crucial evidence for reducing misdiagnosis.\u003c/p\u003e \u003cp\u003eThe model offers unambiguous guidance for clinical management. For patients suspected of SO, particularly those with fertility desires or during pregnancy, this can reinforce confidence in follow-up observation and avoid unnecessary laparotomy. Thus, this tool facilitates customized surgical strategies and provides a framework for precise preoperative risk stratification and management. Nevertheless, several limitations should be acknowledged. Double reading and expert confirmation improved the consistency, but retrospective design may not eliminate subjective bias. Single-center dataset without external validation also limited generalizability. Future multicenter prospective studies are needed to further verify robustness and facilitate broader clinical adoption. Moreover, the model currently distinguishes only SO from HGSC, subsequent studies must include other malignant epithelial malignancy to enhance clinical utility.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe developed and validated the first static and dynamic nomogram for differentiate O-RADS US 4/-5 SO from early-stage HGSC, providing a visualized decision support tool for a major preoperative diagnostic challenge. From a pathophysiological perspective, we elucidated the key morphological patterns of SO, systematically summarized three SO-specific ultrasonographic signs, and reinterpreted the \u0026ldquo;pearl sign\u0026rdquo;. In short, these findings may support more accurate, strategic preoperative decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStruma ovarii\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUltrasound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eO-RADS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian-Adnexal Reporting and Data System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-grade serous ovarian carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Federation of Gynecology and Obstetrics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBilateral salpingo-oophorectomy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIOTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Ovarian Tumor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study followed the Declaration of Helsinki and was approved by both the Institutional Ethics Committee (approval No. JS-3232) and the Institutional Review Board (approval No. I-24PJ0809) of Peking Union Medical College Hospital. All methods were performed in accordance with relevant guidelines and regulations. The requirement for written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication was obtained from all individual participants whose data are included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from thecorresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2023YFC2411705), the National Natural Science Foundation of China (U22A2023, 62325112, and 82402293), the PUMCH Talent Development Support Program (ULJ04684), and the National High-Level Hospital Clinical Research Funding (2022-PUMCH-D-002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed significantly to this work. Wanting Chen and Fei Ji contributed equally to this work and share first authorship. They were involved in study conception, data collection, data analysis, and manuscript drafting. Weihan Xiao and Jiajia Jiajia participated in data collection, data sorting, and result interpretation. Wenjing Zhao was responsible for scientific illustration and chart drawing. Jinghui Liang contributed to data collection and literature collection. Na Su and Meng Yang are the corresponding authors, responsible for study design, supervision, critical revision of the manuscript, and final approval of the version to be published. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWillemse PH, Oosterhuis JW, Aalders JG, et al. 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Int J Womens Health. 2022;14:1291\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/ijwh.S379128\u003c/span\u003e\u003cspan address=\"10.2147/ijwh.S379128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Struma ovarii, high-grade serous ovarian carcinoma, ultrasound signs, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-9459948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9459948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eStruma ovarii (SO) often presents as a multilocular-solid adnexal mass with abundant vascularity, easily misdiagnosed as an epithelial malignancy and leading to inappropriate surgical management. This study aimed to develop and validate a preoperative nomogram to differentiate O-RADS US 4/5 struma ovarii (SO) from early-stage high-grade serous carcinoma (HGSC) and to identify characteristic ultrasound signs of SO, enhancing diagnostic accuracy and clinical applicability.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study enrolled 93 SO lesions from 92 patients and 87 early-stage HGSC lesions from 79 patients at Peking Union Medical College Hospital between January 2017 and December 2025. Lesions were randomly divided into training and validation sets (7:3). Independent predictors were identified using univariate and multivariate logistic regression. An individualized risk prediction nomogram was then developed, along with a dynamic nomogram constructed using the \"DynNom\" and \"shiny\" packages in R. Model performance was evaluated using the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong SO cases, 65 (70.7%) were asymptomatic, 23 (25.0%) had elevated CA125 (\u0026gt;\u0026thinsp;35 U/mL). Ascites was present in 7 patients (7.6%). 80 lesions (86.0%) were classified as O-RADS 5, and 25 (26.9%) misdiagnosed as malignant. Most SO were pure lesions with low malignant transformation and no metastasis. 9 premenopausal patients (17.3%) underwent radical surgery, suggesting overtreatment. Multivariate logistic regression identified CA125, heterogeneous cyst fluid, comet-tail artifact, morphological pattern of the solid component, and echogenicity of the solid component as independent preoperative predictors. The clinical-ultrasound nomogram demonstrated excellent predictive performance, with an AUC of 0.971 (95% CI: 0.95\u0026ndash;0.99) in the training set and 0.939 (95% CI: 0.88\u0026ndash;0.99) in the validation set. Calibration curves indicated strong agreement between predicted and observed probabilities, and decision-curve analysis confirmed its clinical utility. Three typical ultrasound signs of SO\u0026mdash;ovary-like sign, palette sign, and Cheerios sign\u0026mdash;were summarized, and the interpretation of the \u0026ldquo;pearl sign\u0026rdquo; was refined.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe static and dynamic nomograms and ultrasound signs enable accurate preoperative differentiation of O-RADS US 4/5 SO from HGSC, which provide an innovative and reproducible framework for precise preoperative decision-making.\u003c/p\u003e","manuscriptTitle":"Individualized risk prediction nomogram and ultrasound signs for preoperative differentiation of struma ovarii","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 17:59:30","doi":"10.21203/rs.3.rs-9459948/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"324666101415801240501121594625697823240","date":"2026-05-06T21:43:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T15:22:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T13:31:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-27T13:30:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2026-04-19T06:56:56+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":"d4c4d22d-c755-4d9b-a1da-1db4141a5cb1","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"324666101415801240501121594625697823240","date":"2026-05-06T21:43:46+00:00","index":22,"fulltext":""},{"type":"reviewersInvited","content":"8","date":"2026-05-05T15:22:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T17:59:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 17:59:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9459948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9459948","identity":"rs-9459948","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}