Risk factor analysis and prediction model construction for contralateral central lymph node metastasis in unilateral papillary thyroid carcinoma

Author:

Qin jihao1,Fang Xiaowen1,Liang Chenxi1,Li Siyu1,Zeng Xueyu1,Jiang Hancheng2,Chen Zhu2,Li Jie-Hua1

Affiliation:

1. Department of Gastrointestinal and Gland Surgery The First Affiliated Hospital of Guangxi Medical University, Nanning 530021,China

2. Department of Thyroid Surgery, the Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou 545027, China

Abstract

Abstract

Objective To investigate contralateral central lymph node metastasis (CCLNM) in patients with unilateral papillary thyroid carcinoma (UPTC). To provide a reference for clinical decision-making, a prediction model for the probability of contralateral central lymph node metastasis was established. Method The clinicopathological data of 221 UPTC patients who underwent surgical treatment were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for CCLNM according to clinicopathological characteristics, construct a prediction model to construct a visual nomogram, and evaluate the model. Results According to univariate and multivariate logistic regression analyses, sex (P = 0.01, OR: 3.790, 95% CI: 1.373–10.465), extrathyroidal tumor extension (ETE) (P = 0.040, OR: 6.364, 95% CI: 1.083–37.381), tumor diameter (P = 0.010, OR: 3.674, 95% CI: 1.372–9.839) and ipsilateral central lymph node metastasis (ICLNM) (P < 0.001, OR: 38.552, 95% CI: 2.675–27.342) were found to be independent risk factors for CCLNM and were used to construct a nomogram for internal verification. The ROC curve had an AUC of 0.852 in the training group and an AUC of 0.848 in the verification group, and the calibration curve indicated that the prediction probability of the model was consistent with the actual probability. Finally, the analysis of the decision curve showed that the model has good application value in clinical decision-making. Conclusion Sex, ETE, tumor size, and ICLNM emerged as independent risk factors for CCLNM in UPTC patients. A predictive model was therefore developed, harnessing these variables to enable an objective, personalized estimation of CCLNM risk. This tool offers valuable insights to inform surgical planning and optimize treatment strategies for UPTC management.

Publisher

Springer Science and Business Media LLC

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