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Model for Predicting Central Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Study Based on Ultrasound
JingWen Zhang1, MingHui Zhang1, ShangYan Xu1
1Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai, Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objective:
To investigate the association between ultrasound (US) viscosity imaging and central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC) and to develop a new model for predicting CLNM based on patient clinical data, grey-scale US characteristics and US viscosity imaging parameters.
Materials And Methods:
This prospective study enrolled patients with PTC who underwent preoperative US viscosity from June 2024 to September 2024. Using histological results as the reference standard, optimal cutoff values for the viscosity parameter were established to predict central lymph node metastasis (CLNM). Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for CLNM. The diagnostic performance of multimodal US in predicting CLNM was evaluated using receiver operating characteristic (ROC) curve analysis.
Results:
A total of 371 patients were evaluated. The maximum viscosity (Vmax) parameter (Pa·s) demonstrated the best diagnostic performance in predicting CLNM (AUC = 0.71, p < 0.01) and a cutoff value of >4.195 Pa·s. Multivariate analysis identified independent factors for predicting CLNM, including younger patient, male gender, larger tumor size, extrathyroidal extension (ETE), multifocality. The AUC of the predictive model based on these risk factors was 0.78 (95% CI: 0.73, 0.81). When Vmax >4.195 Pa·s was combined with clinical predictors for CLNM, the AUC significantly improved to 0.81 (95% CI: 0.77, 0.86; p = 0.02).
Conclusion:
As a complement to gray-scale US, preoperative US viscosity imaging may serve as a valuable tool for predicting CLNM in patients with PTC.
