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

Objective To 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. 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 < 20%, 20–40%, 40–60%, 60–80%, > 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.