Improving diagnosis and management of pediatric ovarian masses: development of a risk stratification model incorporating sonographic and clinical features

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Methods This single-center retrospective study analyzed 344 consecutive patients ≤ 18 years undergoing ovarian surgery (2018–2024). Three blinded radiologists assessed ultrasonographic parameters: maximum mass diameter and solid component proportion (Categorized as 80%). Multivariate logistic regression integrated clinical features, tumor markers, and sonographic variables to construct a malignancy prediction model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. Results Germ cell tumors(GCTs) predominated (72.7%, 253/348), with malignant lesions comprising 11.5% (40/348). Solid component proportion > 80% was the strongest malignancy predictor (odds ratio [OR] = 576.5, 95% confidence intervals [CI]:74.0–4,492.6; *p*<0.001). The combined model (Mass size + Solid component proportion) achieved superior diagnostic accuracy (Area under the curve [AUC] = 0.93, sensitivity 87.5%, specificity 83.2%), outperforming single parameters (Solid component proportion AUC = 0.86; Mass size AUC = 0.76). In addition to key clinical discriminators such as older age, absence of precocious puberty, and larger tumor size, the exclusive presence of sonographic features like septations (28.3%) and calcifications (5.7%) in epithelial tumors (*p* < 0.001 vs. follicular cysts) provides a reliable basis for differentiation, enabling a significant reduction in unnecessary surgeries for physiological cysts. Conclusion This study establishes an evidence-based prediction model for Chinese pediatric ovarian tumors, redefining malignancy risk stratification through quantitative ultrasonographic thresholds. Furthermore, it identifies key discriminators (Septations, Calcifications, alongside Age, precocious puberty and Mass size) to differentiate physiological follicular cysts from neoplastic epithelial tumors. The integration of solid component proportion > 40% and tumor biomarkers optimizes preoperative decision-making, which can significantly reduce unwarranted surgery for benign conditions while ensuring timely intervention for high-risk cases. Ovarian mass Pediatric surgery Ovarian malignancy Sonography Gynecology Figures Figure 1 Figure 2 Figure 3 Introducton Ovarian tumors represent the most prevalent gynecological neoplasms in children and adolescents, accounting for 1–2% of all pediatric malignancies. Strikingly distinct from adults—where epithelial carcinomas predominate—pediatric cases are dominated by GCTs (60–70%), predominantly benign mature teratomas, creating critical opportunities for fertility-preserving management [ 1 , 2 ] . In the context of pediatric ovarian malignancies, evidence confirms that early diagnosis confers significantly improved prognosis, even for advanced-stage tumors [ 3 , 4 ] .However, three persistent clinical dilemmas hinder progress: First, adult malignancy prediction tools (e.g., The risk of ovarian malignancy algorithm [ROMA], Assessment of different neoplasias in the adneXa [ADNEX] models) exhibit limited validity in children,primarily because both models target epithelial carcinomas, whereas GCTs dominate pediatric cases. Furthermore, ROMA/ADNEX ignore alpha fetoprotein (AFP) and β-human chorionic gonadotrophin (β-HCG) —elevated in most pediatric malignancies here—while over-relying on Carbohydrate antigen 125 (CA125) and Human epididymis protein 4 (HE4) [ 5 , 6 ] . Second, sonographic similarities between functional follicular cysts and surgically relevant epithelial tumors lead to unnecessary resections of physiological cysts, causing irreversible fertility damage. Third, current studies remain limited by small samples, while large-scale histopathological data from Chinese cohorts remain scarce despite the potential for ethnic variations in tumor distribution. These gaps perpetuate reliance on subjective clinical judgment, leading to inconsistent surgical decision-making. Leveraging a single-center cohort of 344 surgical cases, this study aimed to: ① Systematically characterize the age-stratified pathological landscape of Chinese pediatric ovarian masses; ② Develop and validate a malignancy prediction model integrating clinical features (Age, Precocious puberty), sonographic parameters (Mass size, Solid component proportion), and tumor markers; ③ Establish objective clinical features and imaging discriminators for follicular cysts versus epithelial tumors. Innovations include: First, the development of a novel, pediatric-specific predictive model that integrates tumor biomarkers (AFP, CA125, β-HCG) with quantified sonographic parameters (e.g., Solid component proportion as graded categorical variables and Mass size), a methodology that significantly improves risk stratification; Second, in addition to patient age and mass size, we identified the presence of septations or calcifications as key discriminators for differentiating epithelial tumors from physiological follicular cysts. Our model optimizes preoperative risk stratification, potentially sparing of low-risk children from unnecessary surgery while ensuring timely intervention for high-risk cases. And the application of these discriminators could significantly reduce unnecessary resections for physiological cysts, thereby mitigating ovarian failure risks. Methods This single-center retrospective cohort study was approved by the Ethics Committee of Children’s Hospital of Soochow University (No. 2025CS184), with waiver of informed consent for anonymized retrospective data analysis. We included all consecutive patients ≤ 18 years who underwent ovarian surgery between January 2018 and December 2024. Exclusion criteria: ①Non-ovarian neoplasms (e.g., Metastatic tumors to ovary ); ②History of ovarian malignancy; ③Incomplete clinical-sonographic data;④Disorders of sexual development. Finally, 344 patients were enrolled. Three blinded pediatric radiologists (> 10 years' experience) assessed maximum diameter (mm) and solid component proportion (5-tiered: 80%). Septations were defined as internal linear echoes ≥ 1 mm; calcifications as hyperechoic foci with acoustic shadowing. Normality of continuous variables was assessed by Shapiro-Wilk test. Normally distributed data presented as mean ± SD (Independent t-test), non-normal as median Interquartile range [IQR] (Mann-Whitney U test). Categorical variables as frequency (%) (χ² or Fisher’s exact test). Binary logistic regression identified malignancy predictors with variable entry criterion: univariate p < 0.05. ROC analysis evaluated diagnostic performance. Analyses used SPSS 26.0 and Graphpad prism 9. Results Among 348 ovarian lesions from 344 pediatric patients undergoing surgery (Including 4 patients with bilateral masses) (Table 1 ), GCTs constituted the predominant pathology (253 cases, 72.7%), followed by epithelial tumors (53 cases, 15.2%), sex cord-stromal tumors (7 cases, 2.0%), and follicular cysts (35 cases, 10.1%).Mature teratoma was the most common subtype (219/253, 86.6%), with immature teratoma (22/253, 8.7%), yolk sac tumor (4/253, 1.6%), dysgerminoma (4/253, 1.6%), and mixed germ cell tumors (4/253, 1.6%) comprising the remainder. Serous cystadenoma (28/53, 52.8%) and mucinous cystadenoma (25/53, 47.2%) were observed in Epithelial tumors. Sex cord-stromal tumors are included juvenile granulosa cell tumor (4/7, 57.1%), Sertoli-Leydig cell tumor (2/7, 28.6%), and fibroma (1/7, 14.3%). Age distribution analysis (Fig. 1 ) revealed epithelial tumors predominantly occurred in the 9-16-year age group (49/53, 92.5%), whereas GCTs and follicular cysts showed no significant age-specific predominance across groups. Table 1 Histopathological classification of 348 pediatric ovarian masses by cell type and age group Histological diagnosis of tumor <1(y) 1–8(y) 9–16(y) Total Proportion a Germ cell 17 113 123 253 72.7% Mature teratoma 16 93 110 Immature teratoma 1 14 7 Yolk sac 0 1 3 Dysgerminoma 0 3 1 Mixed germ cell tumors 0 2 2 Epithelial 0 4 49 53 15.2% Serous cystadenoma 0 2 26 Mucinous cystadenoma 0 2 23 Sex-cord stromal 0 5 2 7 2.0% Juvenile granulosa cell 0 3 1 Sertoli-Leydig 0 2 0 Fibroma 0 0 1 Follicular cyst 3 16 16 35 10.1% a Values are n (%) of 348 cases After excluding follicular cysts (n = 35), 313 ovarian masses from 309 patients were classified by malignancy status, comprising 273 benign lesions (Including 4 bilateral cases) and 40 malignant lesions. Comparative analysis of clinical characteristics, laboratory markers, and ultrasound parameters is presented in Table 2 . Malignant cases occurred at a significantly younger age than benign cases (7.4 ± 2.9 years vs 8.8 ± 3.8 years; p = 0.01). No significant difference in laterality was observed ( p = 0.07). Abdominal pain was the most common presenting symptom in both groups (Benign:54.3%, Malignant:52.5%; p = 0.834), while precocious puberty was significantly more frequent in malignant cases (20.0% vs 8.6%; p = 0.025). Tumor marker analysis revealed significantly higher rates of elevated AFP, CA-125, and β-HCG in malignant tumors (All p < 0.001). Carcinoembryonic antigen (CEA) was undetectable in all tested cases, and no significant difference in estradiol levels was found between groups ( p = 0.063). Ultrasound measurements demonstrated larger maximum diameters in malignant masses (105.4 ± 43.7 mm vs 67.7 ± 45.5 mm; p < 0.001). Benign lesions predominantly exhibited < 20% solid component proportion (77.7%), whereas malignant lesions showed progressively higher solidity proportions ( p < 0.001 for trend). Table 2 Comparison of Clinical Characteristics, Tumor Markers, and Ultrasound Features Between Malignant and Benign Ovarian Masses Benign Malignant P No. of Patients 269 40 No. of Cases 273 40 Age(years), mean ± SD 8.8 ± 3.8 7.4 ± 2.9 0.01 Laterality, Right-sided, n (%) Right: 151 (55.3%) Right: 16(40.0%) 0.07 Chief complaint, n (%) Pain 146 (54.3%) 21 (52.5%) 0.834 Mass 12 (4.5%) 4 (10.0%) 0.275 Precocious puberty 23 (8.6%) 8 (20.0%) 0.025 Incidental finding 88 (32.7%) 7 (17.5%) 0.052 Tumor marker s, n/N(%) a AFP 1/124 (0.8%) 19/23 (82.6%) <0.001 CEA 0/124 (0%) 0/23 (0%) CA−125 12/102 (11.8%) 14/18 (77.8%) <0.001 Estradiol 8/43 (18.6%) 6/11 (54.6%) 0.063 β -HCG 0/85 (0%) 6/15 (40.0%) <0.001 Mass size by Ultrasound(mm), mean ± SD 67.7 ± 45.5 105.4 ± 43.7 <0.001 Solid component proportion (Ultrasound imaging), n (%) <20% 212 (77.7%) 6 (15.0%) <0.001 20%−40% 42 (15.4%) 8 (20.0%) 0.457 40%−60% 8 (2.9%) 11 (27.5%) 80% 2 (0.7%) 11 (27.5%) <0.001 Fisher’s exact test was used for groups with expected cell counts < 5 Tumor marker elevation was defined as serum levels exceeding institutional reference ranges. a N indicates number of patients tested Binary logistic regression analysis was performed using age(years), precocious puberty, mass size(mm), and solid component proportion (Categorized as 40–60% and > 80% with 80% as key predictors of malignancy. Age demonstrated an inverse association with malignancy (OR = 0.8, 95% CI: 0.6–0.9; p = 0.003). Increased mass size was significantly associated with a higher risk of malignancy (OR = 1.03 per mm, 95% CI: 1.02–1.04; p < 0.001). Both solid component proportion 40–60% (OR = 99.0, 95% CI: 19.4–505.5; p 80% (OR = 576.5, 95% CI: 74.0–4492.6; p < 0.001) were the strongest predictors of malignant ovarian tumors. Although precocious puberty did not reach statistical significance ( p = 0.109), it showed an increased risk of malignancy (OR = 3.6, 95% CI: 0.8–17.2). ROC curve analysis was performed to evaluate the diagnostic performance of ultrasound parameters-mass size, solid component proportion, and their combination-in differentiating malignant from benign ovarian tumors (Fig. 3 and Table 3 ). The solid component proportion demonstrated significantly higher diagnostic efficacy (AUC = 0.86; p < 0.001) compared to mass size alone (AUC = 0.76; p < 0.001). However, the combined model achieved excellent predictive power (AUC = 0.93; p < 0.001), substantially outperforming either single parameter. Table 3 Diagnostic Performance for Differentiating Malignant from Benign Pediatric Ovarian Masses Using ROC Curve Analysis Parameter AUC p Cut-off value Sensitivity(%) Specifici(%) Mass size 0.76 <0.001 85.5 (mm) 67.5 77.3 Solid component 0.86 30% 85.0 77.7 Combined model a 0.93 <0.001 0.097 87.5 83.2 a The combined model refers to the predicted probability derived from the multivariate logistic regression model including mass size and solid component proportion. To establish reliable discriminators for reducing unnecessary surgery, we analyzed 53 epithelial tumors and 35 follicular cysts (Table 4 ). Patients with epithelial tumors were significantly older than those with follicular cysts (median age: 12 years [IQR 10–13] vs 8 years [IQR 1–12]; p < 0.001). Laterality analysis revealed a distinct distribution pattern, with follicular cysts exhibiting a predilection for the right ovary (74.3% vs 52.8% in epithelial tumors; p = 0.043). Precocious puberty was more prevalent in the follicular cyst group (37.1% vs 13.2%; p = 0.009). Ultrasound measurements revealed larger maximum diameters in epithelial tumors (78 mm [IQR 54–107] vs 50 mm [IQR 44–59]; p < 0.001). While both types predominantly presented as purely cystic lesions (Epithelial: 66.0%, Follicular: 100%), septations (28.3% of epithelial cysts) and psammomatous calcifications (5.7%) were exclusively observed in epithelial tumors. Table 4 Comparison of Characteristics Between Epithelial Tumors and Follicular Cysts in Pediatric Ovarian Masses Epithelial Follicular cyst P No. of Patients 53 35 No. of Cases 53 35 Age(years), IQR 12 (10,13) 8 (1,12) <0.001 Laterality, Right-sided, n (%) Right:28 (52.8%) Right:26 (74.3%) 0.043 Chief complaint, n (%) Pain 28 (52.8%) 13 (37.1%) 0.149 Mass 5 (9.4%) 3 (8.6%) 1.000 Precocious puberty 7 (13.2%) 13 (37.1%) 0.009 Incidental finding 13 (24.5%) 6 (17.1%) 0.410 Mass size(mm), IQR 78 (54,107) 50 (44,59) <0.001 Imaging characteristic (Ultrasound), n(%) Purely cystic 35 (66.0%) 35 (100%) <0.001 Presence with calcification 3 (5.7) 0 (0%) 0.273 Presence with septations 15 (28.3%) 0 (0%) <0.001 Abbreviation: IQR, interquartile range Fisher’s exact test was used for groups with expected cell counts < 5 Discussion This study establishes a malignancy prediction model for pediatric ovarian tumors based on a large Chinese cohort (n = 344).Key discoveries include: ①GCTs dominate the pathological spectrum (72.7%), with mature teratoma comprising 86.6%; ② Solid component proportion > 80% emerged as the strongest malignancy predictor (OR = 576.5, p < 0.001); ③The combined model (Mass size + Solid component proportion) significantly improved diagnostic accuracy (AUC = 0.932 vs 0.86 for solid component proportion alone); ④For follicular cyst discrimination, septations (28.3%) and calcifications (5.7%) were specific to epithelial tumors (p < 0.001). These findings address the critical evidence gap in Chinese pediatric ovarian oncology. This study confirms that GCTs constitute the predominant pathological type of ovarian masses in Chinese children, accounting for 72.7% (253/348) of cases. Within GCTs, mature teratoma was the most prevalent subtype (86.6%, 219/253), aligning closely with epidemiological data from Western populations [ 7 ] . Epithelial tumors represented the second most common category (15.2%, 53/348). Notably, epithelial tumors demonstrated a striking age-dependent prevalence, with 92.5% (49/53) occurring in the 9–16 year age group. This pattern may be attributed to gonadal maturation and dynamic hormonal changes characteristic of this developmental stage [ 8 , 9 ] .The overall malignancy rate in our surgical cohort was 11.5% (40/348). Among malignant lesions, GCTs were again predominant (85.0%, 34/40), with immature teratoma being the most frequent malignant subtype (55.0% of malignant GCTs, 22/40). This distribution underscores the fundamental pathological divergence between pediatric and adult ovarian tumors, where epithelial carcinomas predominate [ 10 ] . However, current clinical focus and prediction tools remain primarily oriented towards adult malignancies [ 5 ] . Consequently, developing efficient, pediatric-specific prediction models for ovarian malignancy, such as the one validated herein, is of paramount clinical significance. Although the initial clinical presentations of ovarian tumors are diverse, abdominal pain represents the most common symptom in both benign and malignant cases. This is largely attributable to the frequent use of ultrasound as the primary screening modality for pediatric abdominal emergencies, leading to the incidental detection of many masses during abdominal scanning [ 11 ] . Consequently, abdominal pain alone cannot serve as a reliable discriminator for tumor malignancy. However, precocious puberty demonstrates a statistically significant difference between benign and malignant tumors. This association is likely attributable to the hormone-secreting properties of specific malignancies, particularly sex cord-stromal tumors such as juvenile granulosa cell tumors [ 12 ] . Regarding tumor markers, although CA-125 levels demonstrated a statistically significant difference between benign and malignant groups (p < 0.001), its sensitivity for malignancy was merely 53.8% (14/26). This finding underscores the inapplicability of adult prediction models like ROMA—which heavily relies on CA-125—to the pediatric population [ 5 ] . Furthermore, HE4, a cornerstone biomarker in ROMA for epithelial carcinomas, is rarely associated with the predominant germ cell tumors seen in children [ 13 ] . These limitations collectively highlight the urgent need to develop malignancy prediction models specifically tailored for children. In the present study, we incorporated AFP and β-HCG, markers highly relevant to pediatric germ cell malignancies. AFP exhibited the highest sensitivity (95.0%, 19/20) for identifying malignant germ cell tumors. This exceptional performance stems from the persistent and excessive production and secretion of AFP by yolk sac tumor elements, a common component within malignant pediatric germ cell tumors [ 3 ] . Similarly, elevated β-HCG levels in children and adolescents (Excluding pregnancy) strongly suggest the presence of trophoblastic components, most frequently encountered in mixed germ cell tumors and immature teratomas [ 14 ] .Critically, postoperative monitoring reveals that declining AFP and β-HCG levels serve as favorable prognostic indicators, whereas elevated or rising levels may indicate tumor recurrence [ 15 ] . While the ADNEX model dichotomizes solid component proportion simply (> 10% as high-risk for malignancy), its adult-centric threshold fundamentally conflicts with pediatric pathology [ 16 ] . Benign lesions in children are predominantly mature teratomas, which frequently contain innocuous solid structures such as fat and calcifications [ 17 ] . This leads to systematic bias in malignancy prediction by ADNEX, resulting in overestimation of risk for benign lesions with solid elements.Our study proposes a paradigm-shifting criterion through innovative quantification: Based on ultrasound assessment of 313 pediatric benign and malignant tumors, we established > 40% solid component proportion as a child-specific malignancy risk threshold (40–60%: OR = 99.0, p 80%: OR = 576.5, p 85.5mm) achieved significantly superior diagnostic efficacy (AUC = 0.932). This finding not only rectifies the limitations of prior models but also establishes a new imaging gold standard for precision risk stratification in pediatric ovarian tumors. Derived from multivariate analysis, our novel risk stratification framework incorporates an ultrasound-first classification protocol. Purely cystic masses (Type A): For purely cystic masses, discrimination between follicular cysts and neoplastic lesions must integrate age, size, precocious puberty manifestations, and the absence/presence of septations or calcifications—critical to avoid unnecessary resections of physiological entities. Solid-containing masses (Type B):① Low-risk: Solid component proportion < 20% + Maximum diameter < 85.5 mm + Absence of elevated AFP, CA125, or β-HCG;② Intermediate-risk: Solid component proportion 20%-40% ± Maximum diameter ≥ 85.5 mm + Absence of elevated AFP, CA125, or β-HCG༛③ High-risk: Solid component proportion > 40% + Maximum diameter ≥ 85.5 mm ± Presence of elevated AFP, CA125, or β-HCG. This system synergistically integrates quantitative ultrasonographic parameters (Solid component proportion, Mass size) and serum biomarkers (AFP, β-HCG, CA125), significantly enhancing diagnostic efficacy for pediatric malignancies and providing critical guidance for surgical decision-making. Although epithelial tumors and follicular cysts share similar sonographic features, their clinical management differs substantially: epithelial tumors typically require surgical resection, whereas follicular cysts are functional entities that often resolve spontaneously [ 18 ] .Follicular cysts arise from immature hypothalamic-pituitary-gonadal (HPG) axis development in young children (< 8 years), rendering them susceptible to external stimuli (e.g., Obesity, Stress) that trigger excessive follicular proliferation without ovulation, ultimately leading to cyst formation [ 19 ] . In contrast, epithelial tumors originate from heightened proliferative activity of ovarian surface epithelium during gonadal maturation in adolescence, which increases cumulative genetic mutations [ 20 ] . Consequently, follicular cysts manifest at significantly younger ages than epithelial tumors. A statistical difference in laterality was observed (p = 0.043). However, we do not consider this to be clinically meaningful. The finding is likely a spurious association attributable to the small sample size in this subset of patients rather than a reflection of true underlying pathophysiology. Unlike epithelial neoplasms, follicular cysts feature granulosa cells that persistently secrete estradiol, resulting in prominent pseudoprecocious puberty [ 19 ] . Due to confinement by the ovarian cortex, follicular cysts undergo spontaneous rupture or absorption upon reaching critical dimensions, whereas epithelial tumors possess inherent proliferative capacity and sustained secretory function—enabling continuous growth unless detected due to acute complications like torsion or rupture [ 21 – 23 ] . Therefore,this fundamental difference in biological behavior explains why epithelial tumors are typically discovered at significantly larger sizes compared to follicular cysts. mucinous cystadenomas, the calcifications might be related to a secretory phenomenon, while true septations form through epithelial cell projections into the lumen supported by fibrovascular cores [ 24 , 25 ] .The mechanistic framework elucidated herein—anchored in developmental endocrinology (HPG axis immaturity) and tumor biology (epithelial mutational accumulation)—validates our discriminative criteria (Age < 8 years, Precocious puberty, Absence of septations/calcifications), enabling a reduction in unnecessary surgeries for physiological cysts while ensuring timely intervention for true neoplasms. Several limitations also warrant consideration. First, the potential diagnostic significance of the ovarian crescent sign was not systematically evaluated in our cohort [ 26 ] . Second, the absence of borderline epithelial tumors in our series (0/53 epithelial cases) contrasts with reported pediatric incidences of around 15%, suggesting possible selection bias or center-specific pathological classification practices [ 27 ] . Most critically, the extraordinarily wide confidence intervals accompanying high odds ratios (e.g., OR = 576.5, 95% CI: 73.97–4,492.60 for solid proportion > 80%) reflect instability in risk estimation attributable to the limited number of malignant cases (n = 40). Nevertheless, these evidence-based advances address critical gaps in pediatric gynecologic oncology and offer a transformative framework for precision management of ovarian masses in children. Conclusion This study successfully developed and validated a pediatric-specific prediction model for discriminating malignant from benign ovarian tumors in Chinese children, integrating ultrasonographic features with clinical and laboratory data. Our findings underscore the predominance of GCTs in this population and establish solid component proportion > 40% as a robust, quantitative imaging marker for malignancy risk stratification. The combined model, incorporating mass size and solid component proportion, demonstrated superior diagnostic accuracy (AUC = 0.932), significantly outperforming individual parameters. Furthermore, in addition to patient age and mass size, the identification of septations and calcifications as features exclusive to epithelial tumors provides a reliable means to differentiate them from physiological follicular cysts, thereby reducing unnecessary surgeries.This model, which provides a practical, evidence-based tool for preoperative decision-making, helps ensure timely intervention for high-risk cases while preserving ovarian function in low-risk patients. Its design is grounded in the distinct pathophysiology of pediatric ovarian masses. Abbreviations GCTs Germ cell tumors ROC Receiver operating characteristic AFP Alpha fetoprotein CEA Carcinoembryonic antigen AUC Area Under the Curve β-HCG β-humanchorionic gonadotrophin CA125 Carbohydrate antigen 125 HE4 Human epididymis protein 4 OR Odds ratio CI Confidence intervals ORMA The Risk of Ovarian Malignancy Algorithm ADNEX Assessment of Different NEoplasias in the adneXa HPG Hypothalamic-pituitary-gonadal IQR Interquartile range Declarations Acknowledgements None Ethics approval and consent to participate The study was performed with approvals from Children’s Hospital of Soochow University institutional board and ethical committee (No. 2025CS184), and was carried out in strict accordance with the relevant guidelines for the acquisition and use of human information and specimens, and the Declaration of Helsinki. Informed consent was waived because of the retrospective nature of the study. Author Contributions: Conceptualization: Likai Chu, Shuangquan Lu. Data curation: Likai Chu, Zhiming Chen,Mingzhi zhang. Formal analysis: Likai Chu, Min Zhang. Funding acquisition: Likai Chu, Shuangquan Lu. Investigation: Likai Chu;Zhiming Chen. Project administration: Likai Chu, Shuangquan Lu. Resources: Likai Chu, Shuangquan Lu. Software:Likai Chu;Mingzhi Zhang. Supervision: Shuangquan Lu, Min Zhang. Validation: Likai Chu, Shuangquan Lu. Visualization: Likai Chu, Shuangquan Lu. Writing—original draft: Likai Chu;Tianna Cai Writing—review & editing: all authors. Consent for publication Not applicable. Competing interests The authors have no competing interests. Availability of data and materials The datasets generated or analyzed during the study are available from the corresponding author on reasonable request. Funding This study was supported by the Suzhou Science and Technology Development Plan(SYS2019090). Clinical trial number not applicable References El Helali A, Kwok GST, Tse KY. 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Calcifications in ovary and endometrium and their neoplasms. Mod Pathol. 2003;16(3):219-222. doi:10.1097/01.MP.0000057236.96797.07 Katzenstein AL, Mazur MT, Morgan TE, Kao MS. Proliferative serous tumors of the ovary. Histologic features and prognosis. Am J Surg Pathol. 1978;2(4):339-355. doi:10.1097/00000478-197812000-00001 Stankovic Z. Ovarian Cysts and Tumors in Adolescents. Obstet Gynecol Clin North Am. 2024;51(4):695-710. doi:10.1016/j.ogc.2024.08.006 Oltmann SC, Garcia N, Barber R, Huang R, Hicks B, Fischer A. Can we preoperatively risk stratify ovarian masses for malignancy?. J Pediatr Surg. 2010;45(1):130-134. doi:10.1016/j.jpedsurg.2009.10.022 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2026 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 03 Nov, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 26 Aug, 2025 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-7459480","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517214005,"identity":"1fe96206-af1d-4767-87fa-545d53e112ea","order_by":0,"name":"Likai Chu","email":"","orcid":"","institution":"Children’s Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Likai","middleName":"","lastName":"Chu","suffix":""},{"id":517214006,"identity":"9d12c6be-5fff-4c8b-bfb8-1ae5be2f4d20","order_by":1,"name":"Zhiming Chen","email":"","orcid":"","institution":"Children’s Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Zhiming","middleName":"","lastName":"Chen","suffix":""},{"id":517214007,"identity":"c8467a11-33e2-4108-bb05-851820d5cfbd","order_by":2,"name":"Mingzhi Zhang","email":"","orcid":"","institution":"Children’s Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Mingzhi","middleName":"","lastName":"Zhang","suffix":""},{"id":517214008,"identity":"3f6e75a5-14f1-4e85-a899-a55a899eb9fd","order_by":3,"name":"Tianna Cai","email":"","orcid":"","institution":"Children’s Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Tianna","middleName":"","lastName":"Cai","suffix":""},{"id":517214009,"identity":"1299b91e-678b-41ad-8b2d-037c34693d95","order_by":4,"name":"Min Zhang","email":"","orcid":"","institution":"Children’s Hospital of Soochow 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13:42:00","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110589,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7459480/v1/faecfa910429e80fe9830589.html"},{"id":92181764,"identity":"5b9f0f97-c5ac-4190-a99a-5b7c2dfb2c2e","added_by":"auto","created_at":"2025-09-25 13:41:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131339,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of ovarian mass pathologies across age groups (\u0026lt;1, 1-8, 9-16 years)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7459480/v1/0e62c9a4b872e6332127d175.png"},{"id":92181765,"identity":"dc9b8bb0-8894-4b23-b79d-d63a16245f4d","added_by":"auto","created_at":"2025-09-25 13:41:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":229608,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariate logistic regression analysis for predictors of malignancy\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7459480/v1/4b6aefb7eae800a7bfad538e.png"},{"id":92181766,"identity":"a2ecbb13-21b2-4d4c-89ca-f7bc0de060af","added_by":"auto","created_at":"2025-09-25 13:41:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184668,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for malignancy prediction\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7459480/v1/e97030f8208daf97f5c0899a.png"},{"id":101153442,"identity":"f9ee6202-9730-4a91-9964-47ad0c1a44b9","added_by":"auto","created_at":"2026-01-26 16:15:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1500859,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7459480/v1/9941074c-dbbb-48a7-8c6a-a0793737966d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving diagnosis and management of pediatric ovarian masses: development of a risk stratification model incorporating sonographic and clinical features","fulltext":[{"header":"Introducton","content":"\u003cp\u003eOvarian tumors represent the most prevalent gynecological neoplasms in children and adolescents, accounting for 1\u0026ndash;2% of all pediatric malignancies. Strikingly distinct from adults\u0026mdash;where epithelial carcinomas predominate\u0026mdash;pediatric cases are dominated by GCTs (60\u0026ndash;70%), predominantly benign mature teratomas, creating critical opportunities for fertility-preserving management\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the context of pediatric ovarian malignancies, evidence confirms that early diagnosis confers significantly improved prognosis, even for advanced-stage tumors\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.However, three persistent clinical dilemmas hinder progress: First, adult malignancy prediction tools (e.g., The risk of ovarian malignancy algorithm [ROMA], Assessment of different neoplasias in the adneXa [ADNEX] models) exhibit limited validity in children,primarily because both models target epithelial carcinomas, whereas GCTs dominate pediatric cases. Furthermore, ROMA/ADNEX ignore alpha fetoprotein (AFP) and β-human chorionic gonadotrophin (β-HCG) \u0026mdash;elevated in most pediatric malignancies here\u0026mdash;while over-relying on Carbohydrate antigen 125 (CA125) and Human epididymis protein 4 (HE4)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Second, sonographic similarities between functional follicular cysts and surgically relevant epithelial tumors lead to unnecessary resections of physiological cysts, causing irreversible fertility damage. Third, current studies remain limited by small samples, while large-scale histopathological data from Chinese cohorts remain scarce despite the potential for ethnic variations in tumor distribution. These gaps perpetuate reliance on subjective clinical judgment, leading to inconsistent surgical decision-making.\u003c/p\u003e\u003cp\u003eLeveraging a single-center cohort of 344 surgical cases, this study aimed to: ① Systematically characterize the age-stratified pathological landscape of Chinese pediatric ovarian masses; ② Develop and validate a malignancy prediction model integrating clinical features (Age, Precocious puberty), sonographic parameters (Mass size, Solid component proportion), and tumor markers; ③ Establish objective clinical features and imaging discriminators for follicular cysts versus epithelial tumors. Innovations include: First, the development of a novel, pediatric-specific predictive model that integrates tumor biomarkers (AFP, CA125, β-HCG) with quantified sonographic parameters (e.g., Solid component proportion as graded categorical variables and Mass size), a methodology that significantly improves risk stratification; Second, in addition to patient age and mass size, we identified the presence of septations or calcifications as key discriminators for differentiating epithelial tumors from physiological follicular cysts.\u003c/p\u003e\u003cp\u003eOur model optimizes preoperative risk stratification, potentially sparing of low-risk children from unnecessary surgery while ensuring timely intervention for high-risk cases. And the application of these discriminators could significantly reduce unnecessary resections for physiological cysts, thereby mitigating ovarian failure risks.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis single-center retrospective cohort study was approved by the Ethics Committee of Children\u0026rsquo;s Hospital of Soochow University (No. 2025CS184), with waiver of informed consent for anonymized retrospective data analysis. We included all consecutive patients\u0026thinsp;\u0026le;\u0026thinsp;18 years who underwent ovarian surgery between January 2018 and December 2024. Exclusion criteria: ①Non-ovarian neoplasms (e.g., Metastatic tumors to ovary ); ②History of ovarian malignancy; ③Incomplete clinical-sonographic data;④Disorders of sexual development. Finally, 344 patients were enrolled.\u003c/p\u003e\u003cp\u003eThree blinded pediatric radiologists (\u0026gt;\u0026thinsp;10 years' experience) assessed maximum diameter (mm) and solid component proportion (5-tiered: \u0026lt;20%, 20\u0026ndash;40%, 40\u0026ndash;60%, 60\u0026ndash;80%, \u0026gt;\u0026thinsp;80%). Septations were defined as internal linear echoes\u0026thinsp;\u0026ge;\u0026thinsp;1 mm; calcifications as hyperechoic foci with acoustic shadowing.\u003c/p\u003e\u003cp\u003eNormality of continuous variables was assessed by Shapiro-Wilk test. Normally distributed data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Independent t-test), non-normal as median Interquartile range [IQR] (Mann-Whitney U test). Categorical variables as frequency (%) (χ\u0026sup2; or Fisher\u0026rsquo;s exact test). Binary logistic regression identified malignancy predictors with variable entry criterion: univariate \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ROC analysis evaluated diagnostic performance. Analyses used SPSS 26.0 and Graphpad prism 9.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong 348 ovarian lesions from 344 pediatric patients undergoing surgery (Including 4 patients with bilateral masses) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), GCTs constituted the predominant pathology (253 cases, 72.7%), followed by epithelial tumors (53 cases, 15.2%), sex cord-stromal tumors (7 cases, 2.0%), and follicular cysts (35 cases, 10.1%).Mature teratoma was the most common subtype (219/253, 86.6%), with immature teratoma (22/253, 8.7%), yolk sac tumor (4/253, 1.6%), dysgerminoma (4/253, 1.6%), and mixed germ cell tumors (4/253, 1.6%) comprising the remainder. Serous cystadenoma (28/53, 52.8%) and mucinous cystadenoma (25/53, 47.2%) were observed in Epithelial tumors. Sex cord-stromal tumors are included juvenile granulosa cell tumor (4/7, 57.1%), Sertoli-Leydig cell tumor (2/7, 28.6%), and fibroma (1/7, 14.3%). Age distribution analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed epithelial tumors predominantly occurred in the 9-16-year age group (49/53, 92.5%), whereas GCTs and follicular cysts showed no significant age-specific predominance across groups.\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\u003eHistopathological classification of 348 pediatric ovarian masses by cell type and age group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistological diagnosis of tumor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;1(y)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;8(y)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u0026ndash;16(y)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProportion \u003csup\u003ea\u003c/sup\u003e\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\u003eGerm cell\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e113\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e123\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e253\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e72.7%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMature teratoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e110\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmature teratoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYolk sac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDysgerminoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed germ cell tumors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEpithelial\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e15.2%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerous cystadenoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMucinous cystadenoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex-cord stromal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.0%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJuvenile granulosa cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSertoli-Leydig\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibroma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollicular cyst\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e10.1%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Values are n (%) of 348 cases\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter excluding follicular cysts (n\u0026thinsp;=\u0026thinsp;35), 313 ovarian masses from 309 patients were classified by malignancy status, comprising 273 benign lesions (Including 4 bilateral cases) and 40 malignant lesions. Comparative analysis of clinical characteristics, laboratory markers, and ultrasound parameters is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Malignant cases occurred at a significantly younger age than benign cases (7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 years vs 8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 years; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). No significant difference in laterality was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07). Abdominal pain was the most common presenting symptom in both groups (Benign:54.3%, Malignant:52.5%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.834), while precocious puberty was significantly more frequent in malignant cases (20.0% vs 8.6%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025). Tumor marker analysis revealed significantly higher rates of elevated AFP, CA-125, and β-HCG in malignant tumors (All \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Carcinoembryonic antigen (CEA) was undetectable in all tested cases, and no significant difference in estradiol levels was found between groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.063). Ultrasound measurements demonstrated larger maximum diameters in malignant masses (105.4\u0026thinsp;\u0026plusmn;\u0026thinsp;43.7 mm vs 67.7\u0026thinsp;\u0026plusmn;\u0026thinsp;45.5 mm; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Benign lesions predominantly exhibited\u0026thinsp;\u0026lt;\u0026thinsp;20% solid component proportion (77.7%), whereas malignant lesions showed progressively higher solidity proportions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend).\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\u003eComparison of Clinical Characteristics, Tumor Markers, and Ultrasound Features Between Malignant and Benign Ovarian Masses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo. of Patients\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\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\u003eNo. of Cases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\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\u003eAge(years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaterality, Right-sided, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRight: 151 (55.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRight: 16(40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChief complaint, n (%)\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\u003ePain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e146 (54.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (52.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecocious puberty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncidental finding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTumor marker\u003c/b\u003es, \u003cb\u003en/N(%)\u003c/b\u003e \u003csup\u003ea\u003c/sup\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\u003eAFP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/124 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19/23 (82.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0/124 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0/23 (0%)\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\u003eCA\u0026minus;125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12/102 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14/18 (77.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstradiol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/43 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6/11 (54.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e-HCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0/85 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6/15 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMass size by Ultrasound(mm), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.7\u0026thinsp;\u0026plusmn;\u0026thinsp;45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105.4\u0026thinsp;\u0026plusmn;\u0026thinsp;43.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolid component proportion (Ultrasound imaging), n (%)\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\u003e\u0026lt;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212 (77.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (15.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20%\u0026minus;40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40%\u0026minus;60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60%\u0026minus;80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eFisher\u0026rsquo;s exact test was used for groups with expected cell counts\u0026thinsp;\u0026lt;\u0026thinsp;5\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eTumor marker elevation was defined as serum levels exceeding institutional reference ranges.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e N indicates number of patients tested\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBinary logistic regression analysis was performed using age(years), precocious puberty, mass size(mm), and solid component proportion (Categorized as 40\u0026ndash;60% and \u0026gt;\u0026thinsp;80% with \u0026lt;\u0026thinsp;20% as reference, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results identified age, mass size, solid component proportion 40\u0026ndash;60% and \u0026gt;\u0026thinsp;80% as key predictors of malignancy. Age demonstrated an inverse association with malignancy (OR\u0026thinsp;=\u0026thinsp;0.8, 95% CI: 0.6\u0026ndash;0.9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Increased mass size was significantly associated with a higher risk of malignancy (OR\u0026thinsp;=\u0026thinsp;1.03 per mm, 95% CI: 1.02\u0026ndash;1.04; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both solid component proportion 40\u0026ndash;60% (OR\u0026thinsp;=\u0026thinsp;99.0, 95% CI: 19.4\u0026ndash;505.5; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u0026gt;\u0026thinsp;80% (OR\u0026thinsp;=\u0026thinsp;576.5, 95% CI: 74.0\u0026ndash;4492.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the strongest predictors of malignant ovarian tumors. Although precocious puberty did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.109), it showed an increased risk of malignancy (OR\u0026thinsp;=\u0026thinsp;3.6, 95% CI: 0.8\u0026ndash;17.2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eROC curve analysis was performed to evaluate the diagnostic performance of ultrasound parameters-mass size, solid component proportion, and their combination-in differentiating malignant from benign ovarian tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The solid component proportion demonstrated significantly higher diagnostic efficacy (AUC\u0026thinsp;=\u0026thinsp;0.86; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to mass size alone (AUC\u0026thinsp;=\u0026thinsp;0.76; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the combined model achieved excellent predictive power (AUC\u0026thinsp;=\u0026thinsp;0.93; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), substantially outperforming either single parameter.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Performance for Differentiating Malignant from Benign Pediatric Ovarian Masses Using ROC Curve Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCut-off value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecifici(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMass size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.5 (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid component\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined model\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e The combined model refers to the predicted probability derived from the multivariate logistic regression model including mass size and solid component proportion.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo establish reliable discriminators for reducing unnecessary surgery, we analyzed 53 epithelial tumors and 35 follicular cysts (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Patients with epithelial tumors were significantly older than those with follicular cysts (median age: 12 years [IQR 10\u0026ndash;13] vs 8 years [IQR 1\u0026ndash;12]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Laterality analysis revealed a distinct distribution pattern, with follicular cysts exhibiting a predilection for the right ovary (74.3% vs 52.8% in epithelial tumors; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043). Precocious puberty was more prevalent in the follicular cyst group (37.1% vs 13.2%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Ultrasound measurements revealed larger maximum diameters in epithelial tumors (78 mm [IQR 54\u0026ndash;107] vs 50 mm [IQR 44\u0026ndash;59]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While both types predominantly presented as purely cystic lesions (Epithelial: 66.0%, Follicular: 100%), septations (28.3% of epithelial cysts) and psammomatous calcifications (5.7%) were exclusively observed in epithelial tumors.\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\u003eComparison of Characteristics Between Epithelial Tumors and Follicular Cysts in Pediatric Ovarian Masses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEpithelial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFollicular cyst\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo. of Patients\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35\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\u003eNo. of Cases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35\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\u003eAge(years), IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (10,13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (1,12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaterality, Right-sided, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRight:28 (52.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRight:26 (74.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChief complaint, n (%)\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\u003ePain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (52.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecocious puberty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncidental finding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMass size(mm), IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (54,107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (44,59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eImaging characteristic\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(Ultrasound), n(%)\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\u003ePurely cystic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (66.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence with calcification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence with septations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviation: IQR, interquartile range\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eFisher\u0026rsquo;s exact test was used for groups with expected cell counts\u0026thinsp;\u0026lt;\u0026thinsp;5\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study establishes a malignancy prediction model for pediatric ovarian tumors based on a large Chinese cohort (n\u0026thinsp;=\u0026thinsp;344).Key discoveries include: ①GCTs dominate the pathological spectrum (72.7%), with mature teratoma comprising 86.6%; ② Solid component proportion\u0026thinsp;\u0026gt;\u0026thinsp;80% emerged as the strongest malignancy predictor (OR\u0026thinsp;=\u0026thinsp;576.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); ③The combined model (Mass size\u0026thinsp;+\u0026thinsp;Solid component proportion) significantly improved diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.932 vs 0.86 for solid component proportion alone); ④For follicular cyst discrimination, septations (28.3%) and calcifications (5.7%) were specific to epithelial tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings address the critical evidence gap in Chinese pediatric ovarian oncology.\u003c/p\u003e\u003cp\u003eThis study confirms that GCTs constitute the predominant pathological type of ovarian masses in Chinese children, accounting for 72.7% (253/348) of cases. Within GCTs, mature teratoma was the most prevalent subtype (86.6%, 219/253), aligning closely with epidemiological data from Western populations\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Epithelial tumors represented the second most common category (15.2%, 53/348). Notably, epithelial tumors demonstrated a striking age-dependent prevalence, with 92.5% (49/53) occurring in the 9\u0026ndash;16 year age group. This pattern may be attributed to gonadal maturation and dynamic hormonal changes characteristic of this developmental stage\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.The overall malignancy rate in our surgical cohort was 11.5% (40/348). Among malignant lesions, GCTs were again predominant (85.0%, 34/40), with immature teratoma being the most frequent malignant subtype (55.0% of malignant GCTs, 22/40). This distribution underscores the fundamental pathological divergence between pediatric and adult ovarian tumors, where epithelial carcinomas predominate\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, current clinical focus and prediction tools remain primarily oriented towards adult malignancies\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Consequently, developing efficient, pediatric-specific prediction models for ovarian malignancy, such as the one validated herein, is of paramount clinical significance.\u003c/p\u003e\u003cp\u003eAlthough the initial clinical presentations of ovarian tumors are diverse, abdominal pain represents the most common symptom in both benign and malignant cases. This is largely attributable to the frequent use of ultrasound as the primary screening modality for pediatric abdominal emergencies, leading to the incidental detection of many masses during abdominal scanning\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Consequently, abdominal pain alone cannot serve as a reliable discriminator for tumor malignancy. However, precocious puberty demonstrates a statistically significant difference between benign and malignant tumors. This association is likely attributable to the hormone-secreting properties of specific malignancies, particularly sex cord-stromal tumors such as juvenile granulosa cell tumors\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRegarding tumor markers, although CA-125 levels demonstrated a statistically significant difference between benign and malignant groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), its sensitivity for malignancy was merely 53.8% (14/26). This finding underscores the inapplicability of adult prediction models like ROMA\u0026mdash;which heavily relies on CA-125\u0026mdash;to the pediatric population\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Furthermore, HE4, a cornerstone biomarker in ROMA for epithelial carcinomas, is rarely associated with the predominant germ cell tumors seen in children\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These limitations collectively highlight the urgent need to develop malignancy prediction models specifically tailored for children. In the present study, we incorporated AFP and β-HCG, markers highly relevant to pediatric germ cell malignancies. AFP exhibited the highest sensitivity (95.0%, 19/20) for identifying malignant germ cell tumors. This exceptional performance stems from the persistent and excessive production and secretion of AFP by yolk sac tumor elements, a common component within malignant pediatric germ cell tumors\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Similarly, elevated β-HCG levels in children and adolescents (Excluding pregnancy) strongly suggest the presence of trophoblastic components, most frequently encountered in mixed germ cell tumors and immature teratomas\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.Critically, postoperative monitoring reveals that declining AFP and β-HCG levels serve as favorable prognostic indicators, whereas elevated or rising levels may indicate tumor recurrence\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile the ADNEX model dichotomizes solid component proportion simply (\u0026gt;\u0026thinsp;10% as high-risk for malignancy), its adult-centric threshold fundamentally conflicts with pediatric pathology\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Benign lesions in children are predominantly mature teratomas, which frequently contain innocuous solid structures such as fat and calcifications\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This leads to systematic bias in malignancy prediction by ADNEX, resulting in overestimation of risk for benign lesions with solid elements.Our study proposes a paradigm-shifting criterion through innovative quantification: Based on ultrasound assessment of 313 pediatric benign and malignant tumors, we established\u0026thinsp;\u0026gt;\u0026thinsp;40% solid component proportion as a child-specific malignancy risk threshold (40\u0026ndash;60%: OR\u0026thinsp;=\u0026thinsp;99.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026gt;80%: OR\u0026thinsp;=\u0026thinsp;576.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A multiparameter model integrating this metric with maximum mass diameter (\u0026gt;\u0026thinsp;85.5mm) achieved significantly superior diagnostic efficacy (AUC\u0026thinsp;=\u0026thinsp;0.932). This finding not only rectifies the limitations of prior models but also establishes a new imaging gold standard for precision risk stratification in pediatric ovarian tumors.\u003c/p\u003e\u003cp\u003eDerived from multivariate analysis, our novel risk stratification framework incorporates an ultrasound-first classification protocol. Purely cystic masses (Type A): For purely cystic masses, discrimination between follicular cysts and neoplastic lesions must integrate age, size, precocious puberty manifestations, and the absence/presence of septations or calcifications\u0026mdash;critical to avoid unnecessary resections of physiological entities. Solid-containing masses (Type B):① Low-risk: Solid component proportion\u0026thinsp;\u0026lt;\u0026thinsp;20% + Maximum diameter\u0026thinsp;\u0026lt;\u0026thinsp;85.5 mm\u0026thinsp;+\u0026thinsp;Absence of elevated AFP, CA125, or β-HCG;② Intermediate-risk: Solid component proportion 20%-40% \u0026plusmn; Maximum diameter\u0026thinsp;\u0026ge;\u0026thinsp;85.5 mm\u0026thinsp;+\u0026thinsp;Absence of elevated AFP, CA125, or β-HCG༛③ High-risk: Solid component proportion\u0026thinsp;\u0026gt;\u0026thinsp;40% + Maximum diameter\u0026thinsp;\u0026ge;\u0026thinsp;85.5 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;Presence of elevated AFP, CA125, or β-HCG. This system synergistically integrates quantitative ultrasonographic parameters (Solid component proportion, Mass size) and serum biomarkers (AFP, β-HCG, CA125), significantly enhancing diagnostic efficacy for pediatric malignancies and providing critical guidance for surgical decision-making.\u003c/p\u003e\u003cp\u003eAlthough epithelial tumors and follicular cysts share similar sonographic features, their clinical management differs substantially: epithelial tumors typically require surgical resection, whereas follicular cysts are functional entities that often resolve spontaneously\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.Follicular cysts arise from immature hypothalamic-pituitary-gonadal (HPG) axis development in young children (\u0026lt;\u0026thinsp;8 years), rendering them susceptible to external stimuli (e.g., Obesity, Stress) that trigger excessive follicular proliferation without ovulation, ultimately leading to cyst formation\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In contrast, epithelial tumors originate from heightened proliferative activity of ovarian surface epithelium during gonadal maturation in adolescence, which increases cumulative genetic mutations\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Consequently, follicular cysts manifest at significantly younger ages than epithelial tumors. A statistical difference in laterality was observed (p\u0026thinsp;=\u0026thinsp;0.043). However, we do not consider this to be clinically meaningful. The finding is likely a spurious association attributable to the small sample size in this subset of patients rather than a reflection of true underlying pathophysiology. Unlike epithelial neoplasms, follicular cysts feature granulosa cells that persistently secrete estradiol, resulting in prominent pseudoprecocious puberty\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Due to confinement by the ovarian cortex, follicular cysts undergo spontaneous rupture or absorption upon reaching critical dimensions, whereas epithelial tumors possess inherent proliferative capacity and sustained secretory function\u0026mdash;enabling continuous growth unless detected due to acute complications like torsion or rupture\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Therefore,this fundamental difference in biological behavior explains why epithelial tumors are typically discovered at significantly larger sizes compared to follicular cysts. mucinous cystadenomas, the calcifications might be related to a secretory phenomenon, while true septations form through epithelial cell projections into the lumen supported by fibrovascular cores\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.The mechanistic framework elucidated herein\u0026mdash;anchored in developmental endocrinology (HPG axis immaturity) and tumor biology (epithelial mutational accumulation)\u0026mdash;validates our discriminative criteria (Age\u0026thinsp;\u0026lt;\u0026thinsp;8 years, Precocious puberty, Absence of septations/calcifications), enabling a reduction in unnecessary surgeries for physiological cysts while ensuring timely intervention for true neoplasms.\u003c/p\u003e\u003cp\u003eSeveral limitations also warrant consideration. First, the potential diagnostic significance of the ovarian crescent sign was not systematically evaluated in our cohort\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Second, the absence of borderline epithelial tumors in our series (0/53 epithelial cases) contrasts with reported pediatric incidences of around 15%, suggesting possible selection bias or center-specific pathological classification practices\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Most critically, the extraordinarily wide confidence intervals accompanying high odds ratios (e.g., OR\u0026thinsp;=\u0026thinsp;576.5, 95% CI: 73.97\u0026ndash;4,492.60 for solid proportion\u0026thinsp;\u0026gt;\u0026thinsp;80%) reflect instability in risk estimation attributable to the limited number of malignant cases (n\u0026thinsp;=\u0026thinsp;40). Nevertheless, these evidence-based advances address critical gaps in pediatric gynecologic oncology and offer a transformative framework for precision management of ovarian masses in children.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study successfully developed and validated a pediatric-specific prediction model for discriminating malignant from benign ovarian tumors in Chinese children, integrating ultrasonographic features with clinical and laboratory data. Our findings underscore the predominance of GCTs in this population and establish solid component proportion\u0026thinsp;\u0026gt;\u0026thinsp;40% as a robust, quantitative imaging marker for malignancy risk stratification. The combined model, incorporating mass size and solid component proportion, demonstrated superior diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.932), significantly outperforming individual parameters. Furthermore, in addition to patient age and mass size, the identification of septations and calcifications as features exclusive to epithelial tumors provides a reliable means to differentiate them from physiological follicular cysts, thereby reducing unnecessary surgeries.This model, which provides a practical, evidence-based tool for preoperative decision-making, helps ensure timely intervention for high-risk cases while preserving ovarian function in low-risk patients. Its design is grounded in the distinct pathophysiology of pediatric ovarian masses.\u003c/p\u003e"},{"header":" Abbreviations","content":"\u003cp\u003eGCTs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Germ cell tumors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAFP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Alpha fetoprotein\u003c/p\u003e\n\u003cp\u003eCEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Carcinoembryonic antigen\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Area Under the Curve\u003c/p\u003e\n\u003cp\u003eβ-HCG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; β-humanchorionic gonadotrophin\u003c/p\u003e\n\u003cp\u003eCA125 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Carbohydrate antigen 125\u003c/p\u003e\n\u003cp\u003eHE4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Human epididymis protein 4\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds ratio\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence intervals\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eORMA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; The Risk of Ovarian Malignancy Algorithm\u003c/p\u003e\n\u003cp\u003eADNEX \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Assessment of Different NEoplasias in the adneXa\u003c/p\u003e\n\u003cp\u003eHPG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hypothalamic-pituitary-gonadal\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interquartile range\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed with approvals from Children’s Hospital of Soochow University institutional board and ethical committee (No. 2025CS184), and was carried out in strict accordance with the relevant guidelines for the acquisition and use of human information and specimens, and the Declaration of Helsinki. Informed consent was waived because of the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Likai Chu, Shuangquan Lu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData curation: Likai Chu, Zhiming Chen,Mingzhi zhang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFormal analysis: Likai Chu, Min Zhang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding acquisition: Likai Chu, Shuangquan Lu.\u003c/p\u003e\n\u003cp\u003eInvestigation: Likai Chu;Zhiming Chen.\u003c/p\u003e\n\u003cp\u003eProject administration: Likai Chu, Shuangquan Lu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResources: Likai Chu, Shuangquan Lu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoftware:Likai Chu;Mingzhi Zhang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupervision: Shuangquan Lu, Min Zhang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValidation: Likai Chu, Shuangquan Lu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVisualization: Likai Chu, Shuangquan Lu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting—original draft: Likai Chu;Tianna Cai\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting—review \u0026amp; editing: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;study was supported by the Suzhou Science and Technology Development Plan(SYS2019090).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEl Helali A, Kwok GST, Tse KY. Adjuvant and post-surgical treatment in non-epithelial ovarian cancer. Best Pract Res Clin Obstet Gynaecol. 2022;78:74-85. doi:10.1016/j.bpobgyn.2021.06.001\u003c/li\u003e\n\u003cli\u003eBa\u0026scaron;ković M, Habek D, Zaninović L, Milas I, Pogorelić Z. The Evaluation, Diagnosis, and Management of Ovarian Cysts, Masses, and Their Complications in Fetuses, Infants, Children, and Adolescents. Healthcare (Basel). 2025;13(7):775. Published 2025 Mar 31. doi:10.3390/healthcare13070775\u003c/li\u003e\n\u003cli\u003eDe Maria F, Amant F, Chiappa V, et al. Malignant germ cells tumor of the ovary. J Gynecol Oncol. 2025;36(3):e108. doi:10.3802/jgo.2025.36.e108\u003c/li\u003e\n\u003cli\u003eGuo H, Chen H, Wang W, Chen L. Clinicopathological Features, Prognostic Factors, Survival Trends, and Treatment of Malignant Ovarian Germ Cell Tumors: A SEER Database Analysis. Oncol Res Treat. 2021;44(4):145-153. doi:10.1159/000509189\u003c/li\u003e\n\u003cli\u003eMolina R, Escudero JM, Aug\u0026eacute; JM, et al. HE4 a novel tumour marker for ovarian cancer: comparison with CA 125 and ROMA algorithm in patients with gynaecological diseases. Tumour Biol. 2011;32(6):1087-1095. doi:10.1007/s13277-011-0204-3\u003c/li\u003e\n\u003cli\u003eMoore RG, McMeekin DS, Brown AK, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2009;112(1):40-46. doi:10.1016/j.ygyno.2008.08.031\u003c/li\u003e\n\u003cli\u003eHermans AJ, Kluivers KB, Janssen LM, et al. Adnexal masses in children, adolescents and women of reproductive age in the Netherlands: A nationwide population-based cohort study. Gynecol Oncol. 2016;143(1):93-97. doi:10.1016/j.ygyno.2016.07.096\u003c/li\u003e\n\u003cli\u003eTsai JY, Saigo PE, Brown C, La Quaglia MP. 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Multimodality imaging and genomics of granulosa cell tumors. Abdom Radiol (NY). 2020;45(3):812-827. doi:10.1007/s00261-019-02172-3\u003c/li\u003e\n\u003cli\u003eZhang C, Hu H, Wang X, Zhu Y, Jiang M. WFDC Protein: A Promising Diagnosis Biomarker of Ovarian Cancer. J Cancer. 2021;12(18):5404-5412. Published 2021 Jul 6. doi:10.7150/jca.57880\u003c/li\u003e\n\u003cli\u003eG\u0026ouml;bel U, Schneider DT, Calaminus G, Haas RJ, Schmidt P, Harms D. Germ-cell tumors in childhood and adolescence. GPOH MAKEI and the MAHO study groups. Ann Oncol. 2000;11(3):263-271. doi:10.1023/a:1008360523160\u003c/li\u003e\n\u003cli\u003eGică N, Peltecu G, Chirculescu R, et al. Ovarian Germ Cell Tumors: Pictorial Essay. Diagnostics (Basel). 2022;12(9):2050. Published 2022 Aug 24. doi:10.3390/diagnostics12092050\u003c/li\u003e\n\u003cli\u003eCui L, Xu H, Zhang Y. 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Front Endocrinol (Lausanne). 2021;12:772349. Published 2021 Nov 17. doi:10.3389/fendo.2021.772349\u003c/li\u003e\n\u003cli\u003eBhuyan G, Arora R, Ahluwalia C, Sharma P. Epithelial-mesenchymal transition in serous and mucinous epithelial tumors of the ovary. J Cancer Res Ther. 2019;15(6):1309-1315. doi:10.4103/jcrt.JCRT_35_18\u003c/li\u003e\n\u003cli\u003eSutton CL, McKinney CD, Jones JE, Gay SB. Ovarian masses revisited: radiologic and pathologic correlation. Radiographics. 1992;12(5):853-877. doi:10.1148/radiographics.12.5.1529129\u003c/li\u003e\n\u003cli\u003eStewart CJR, Harding S. Stromal Endocrine Cell Micronests Associated With an Ovarian Mucinous Cystadenoma: Endocrine Cell Preservation (Pseudohyperplasia) Potentially Mimicking Stromal Sex Cord Proliferation or Tumor Microinvasion. Int J Gynecol Pathol. 2021;40(1):56-59. doi:10.1097/PGP.0000000000000646\u003c/li\u003e\n\u003cli\u003eSilva EG, Deavers MT, Parlow AF, Gershenson DM, Malpica A. Calcifications in ovary and endometrium and their neoplasms. Mod Pathol. 2003;16(3):219-222. doi:10.1097/01.MP.0000057236.96797.07\u003c/li\u003e\n\u003cli\u003eKatzenstein AL, Mazur MT, Morgan TE, Kao MS. Proliferative serous tumors of the ovary. Histologic features and prognosis. Am J Surg Pathol. 1978;2(4):339-355. doi:10.1097/00000478-197812000-00001\u003c/li\u003e\n\u003cli\u003eStankovic Z. Ovarian Cysts and Tumors in Adolescents. Obstet Gynecol Clin North Am. 2024;51(4):695-710. doi:10.1016/j.ogc.2024.08.006\u003c/li\u003e\n\u003cli\u003eOltmann SC, Garcia N, Barber R, Huang R, Hicks B, Fischer A. Can we preoperatively risk stratify ovarian masses for malignancy?. J Pediatr Surg. 2010;45(1):130-134. doi:10.1016/j.jpedsurg.2009.10.022\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ovarian mass, Pediatric surgery, Ovarian malignancy, Sonography, Gynecology","lastPublishedDoi":"10.21203/rs.3.rs-7459480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7459480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo develop and validate a pediatric-specific prediction model for discriminating malignant from benign ovarian tumors in Chinese children, aiming to reduce unnecessary surgeries for physiological follicular cysts.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis single-center retrospective study analyzed 344 consecutive patients\u0026thinsp;\u0026le;\u0026thinsp;18 years undergoing ovarian surgery (2018\u0026ndash;2024). Three blinded radiologists assessed ultrasonographic parameters: maximum mass diameter and solid component proportion (Categorized as \u0026lt;\u0026thinsp;20%, 20\u0026ndash;40%, 40\u0026ndash;60%, 60\u0026ndash;80%, \u0026gt;\u0026thinsp;80%). Multivariate logistic regression integrated clinical features, tumor markers, and sonographic variables to construct a malignancy prediction model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eGerm cell tumors(GCTs) predominated (72.7%, 253/348), with malignant lesions comprising 11.5% (40/348). Solid component proportion\u0026thinsp;\u0026gt;\u0026thinsp;80% was the strongest malignancy predictor (odds ratio [OR]\u0026thinsp;=\u0026thinsp;576.5, 95% confidence intervals [CI]:74.0\u0026ndash;4,492.6; *p*\u0026lt;0.001). The combined model (Mass size\u0026thinsp;+\u0026thinsp;Solid component proportion) achieved superior diagnostic accuracy (Area under the curve [AUC]\u0026thinsp;=\u0026thinsp;0.93, sensitivity 87.5%, specificity 83.2%), outperforming single parameters (Solid component proportion AUC\u0026thinsp;=\u0026thinsp;0.86; Mass size AUC\u0026thinsp;=\u0026thinsp;0.76). In addition to key clinical discriminators such as older age, absence of precocious puberty, and larger tumor size, the exclusive presence of sonographic features like septations (28.3%) and calcifications (5.7%) in epithelial tumors (*p* \u0026lt; 0.001 vs. follicular cysts) provides a reliable basis for differentiation, enabling a significant reduction in unnecessary surgeries for physiological cysts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study establishes an evidence-based prediction model for Chinese pediatric ovarian tumors, redefining malignancy risk stratification through quantitative ultrasonographic thresholds. Furthermore, it identifies key discriminators (Septations, Calcifications, alongside Age, precocious puberty and Mass size) to differentiate physiological follicular cysts from neoplastic epithelial tumors. The integration of solid component proportion\u0026thinsp;\u0026gt;\u0026thinsp;40% and tumor biomarkers optimizes preoperative decision-making, which can significantly reduce unwarranted surgery for benign conditions while ensuring timely intervention for high-risk cases.\u003c/p\u003e","manuscriptTitle":"Improving diagnosis and management of pediatric ovarian masses: development of a risk stratification model incorporating sonographic and clinical features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 13:41:54","doi":"10.21203/rs.3.rs-7459480/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-03T10:51:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T12:41:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139122402913840502609782394073888150484","date":"2025-09-18T16:08:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T11:53:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196661723691880246083204396717993537006","date":"2025-09-16T15:26:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T15:13:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T10:59:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T10:57:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-08-26T06:27:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1246f27a-a2c7-4ecb-8f47-f16aee73c20b","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:14:00+00:00","versionOfRecord":{"articleIdentity":"rs-7459480","link":"https://doi.org/10.1186/s12887-025-06488-6","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2026-01-22 15:57:42","publishedOnDateReadable":"January 22nd, 2026"},"versionCreatedAt":"2025-09-25 13:41:54","video":"","vorDoi":"10.1186/s12887-025-06488-6","vorDoiUrl":"https://doi.org/10.1186/s12887-025-06488-6","workflowStages":[]},"version":"v1","identity":"rs-7459480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7459480","identity":"rs-7459480","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}