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Updated: Nov 16, 2025
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
Risk Factors and Prediction Model for Lateral Lymph Node Metastasis of Papillary Thyroid Carcinoma in Children and
Weili Liang1, Lei Sheng1, Liguang Zhou2
1Department of Thyroid Surgery, General Surgery, Qilu Hospital of Shandong University, Jinan, 250012, People's Republic of China.
Purpose:
Papillary thyroid carcinoma (PTC) in children and adolescents is prone to lateral lymph node metastasis (LNM), which is a high-risk factor for recurrence. However, few studies focused on identifying risk factors and establishing prediction models for lateral LNM of PTC in children and adolescents.
Patients And Methods:
We retrospectively reviewed consecutive cases of children and adolescents with PTC undergoing thyroidectomy and cervical lymph node dissection between January 2009 and December 2019. The demographics and clinicopathologic features were collected and analyzed.
Results:
A total of 102 children and adolescents with PTC were enrolled in our study; 51 of whom had lateral LNM (50%). After adjusting for other risk factors, the independent risk factors for lateral LNM were multifocality (odds ratio [OR]: 6.04; 95% confidence interval [CI]: 1.653-22.092; p=0.007), tumor size (OR: 1.752; 95% CI: 1.043-2.945; p=0.034), and the number of central LNM (OR: 1.23; 95% CI: 1.028-1.472; p=0.023). The formula of the combined predictor is: Multifocality + 0.31 × Tumor size + 0.115 × Number of central LNM. The area under the receiver operating characteristic curve of multifocality, tumor size, number of central LNM, and the combined predictor was 0.706, 0.762, 0.748, and 0.855, respectively. When the value of the combined predictor was ≥2.2744, lateral LNM could be predicted. The sensitivity and specificity of the predicted value were 82.4% and 74.5%, respectively.
Conclusion:
The independent risk factors for lateral LNM in children and adolescents with PTC were multifocality, tumor size, and the number of central LNM. The prediction model can better predict the presence of lateral LNM.
