Related Experiment Video
Updated: Jul 2, 2025
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
A nomogram based on clinicopathological and ultrasound characteristics to predict central neck lymph node metastases
Fei Chen1, Shuiping Jiang2, Fan Yao1
1General Surgery Center Department of Thyroid Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Purpose:
Papillary thyroid cancer (PTC) has grown rapidly in prevalence over the past few decades, and central neck lymph node metastasis (CNLNM) is associated with poor prognoses. However, whether to carry out preventive central neck lymph node dissection (CNLND) is still controversial. We aimed to construct a prediction model of CNLNM to facilitate making clinical surgical regimens.
Methods:
A total of 691 patients with PTC between November 2018 and December 2021 were included in our study. Univariate and multivariate analyses were performed on basic information and clinicopathological characteristics, as well as ultrasound characteristics (American College of Radiology (ACR) scores). The prediction model was constructed and performed using a nomogram, and then discriminability, calibrations, and clinical applicability were evaluated.
Results:
Five variables, namely, male, age >55 years, clinical lymph node positivity, tumor size ≥1 cm, and ACR scores ≥6, were independent predictors of CNLNM in the multivariate analysis, which were eventually included to construct a nomogram model. The area under the curve (AUC) of the model was 0.717, demonstrating great discriminability. A calibration curve was developed to validate the calibration of the present model by bootstrap resampling, which indicated that the predicted and actual values were in good agreement and had no differentiation from the ideal model. The decision curve analysis (DCA) indicated that the prediction model has good clinical applicability.
Conclusions:
Our non-invasive prediction model combines ACR scores with clinicopathological features presented through nomogram and has shown good performance and application prospects for the prediction of CNLNM in PTCs.
