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

## TL;DR

This study creates a model to distinguish between benign and malignant ovarian masses in Chinese children using ultrasound and clinical features to avoid unnecessary surgeries.

## Contribution

A pediatric-specific prediction model for ovarian mass malignancy using sonographic and clinical features in Chinese children.

## Key findings

- A combined model of mass size and solid component proportion achieved high diagnostic accuracy (AUC = 0.93) for predicting malignancy.
- Solid component proportion > 80% was the strongest predictor of malignancy (OR = 576.5).
- Sonographic features like septations and calcifications help differentiate benign follicular cysts from epithelial tumors.

## Abstract

To develop and validate a pediatric-specific prediction model for discriminating malignant from benign ovarian masses in Chinese children, aiming to reduce unnecessary surgeries for physiological follicular cysts.

This single-center retrospective study analyzed 344 consecutive patients ≤ 18 years undergoing ovarian surgery (2018–2024). Three blinded radiologists assessed sonographic 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.

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.

This study establishes an evidence-based prediction model for Chinese pediatric ovarian masses, redefining malignancy risk stratification through quantitative sonographic 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.