Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma

Abstract

Background: Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM).

Methods: The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1^st, 2019 to December 30^th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram.

Results: A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present.

Conclusion: CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.

Keywords: Central lymph node dissection (CLND); Decision curve analysis (DCA); Nomogram; Papillary thyroid microcarcinoma (PTMC).

Fig. 1

LASSO coefficient profiles of the risk factors with P < 0.05 (A). The results of the fivefold cross-validation determined the optimal value of the penalty parameter λ (λ = 0.002157022). Five independent prognostic genes for risk model construction were identified (B)

Fig. 2

Clinical prognostic nomogram for risk of CLNM. Clinical prognostic nomogram was applied to predict the risk of CLNM by gender, age, tumor size, tumor multifocality and tumor boundary

Fig. 3

The ROC curve of nomogram in PTMC patients: The AUC of the nomogram model in the training set was 0.703 (A), and that in the validation set was 0.656 (B)

Fig. 4

The calibration curve of nomogram in PTMC patients. The calibration curve in the training set (A) and validation set (B)

Fig. 5

The decision curve analysis of nomogram model in PTMC patients