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Updated: Jun 26, 2025
Transoral Robotic Total Thyroidectomy and Bilateral Central Regional Lymph Node Dissection for Papillary Thyroid Carcinoma
Published on: September 15, 2023
Development and validation of a nomogram for preoperative prediction of ipsilateral cervical central lymph node
Dan Zhao1, Wei Li2, Xiaomei Zhang1
1Department of Endocrinology, Peking University International Hospital, Beijing, China.
Background:
The incidence of papillary thyroid cancer (PTC) has increased dramatically, and it is susceptible to cervical lymph node metastasis (LNM), predominantly in the ipsilateral cervical central lymph node metastasis (CLNM). Ipsilateral cervical CLNM affects patients' surgical options and survival rates. In this study, we integrated multiple factors to establish a nomogram-based preoperative prediction model of ipsilateral cervical CLNM in PTC.
Methods:
Data were retrospectively collected from 609 patients with PTC admitted to Peking University International Hospital, all of whom underwent ipsilateral cervical lymph node dissection. They were randomly divided into a modeling set and validation set in the ratio of 7:3. Binary logistic regression was used to analyze independent risk factors for ipsilateral cervical CLNM in PTC and to construct a nomogram model. The performance of nomogram CLNM prediction was evaluated by the receiver operating characteristic (ROC) curve and calibration curve.
Results:
Binary Logistic Regression showed that age, history of osteoporosis, complicated by Hashimoto's thyroiditis, enlarged lymph nodes in the central neck, and extrathyroidal extension were risk factors for ipsilateral cervical CLNM. Combining these five independent risk factors, a nomogram prediction model was developed. In the modeling set, the area under the curve (AUC) of the nomogram ROC was 0.782 [95% confidence interval (CI): 0.730-0.833], and the sensitivity and specificity of the model were 0.761 and 0.763, respectively, with a well-calibrated curve fit. Moreover, the model presented better discrimination than any of the independent risk factors. The nomogram performed well in the validation set (AUC 0.753; 95% CI: 0.648-0.858).
Conclusions:
A non-invasive, and accurate nomogram prediction model for ipsilateral cervical CLNM of PTC was established. It can help physicians identify patients with a high risk of ipsilateral cervical CLNM of PTC preoperative for individualized treatment.
