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

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.

Authors

Institutions

Publication Details

Journal
BMC Pediatrics
Published
2026-01-22
DOI
https://doi.org/10.1186/s12887-025-06488-6
Primary Topic
Ovarian cancer diagnosis and treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Shuangquan Lu, Mingzhi Zhang, Likai Chu, Tianna Cai et al.
BMC Pediatrics
Ovarian cancer diagnosis and treatment
article

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

Shuangquan Lu, Mingzhi Zhang, Likai Chu, Tianna Cai, Min Zhang
article en

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.

BMC PediatricsVol. 26(1)
Soochow University (CN)
Reduced inequalities
Openalex Percentile: Top 82%
Ovarian cancer diagnosis and treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.