Objective To establish a prediction model for ipsilateral cervical lateral lymph node metastasis (LLNM) in stages T1–T2 unilateral papillary thyroid carcinoma (PTC) and to verify its efficacy. Methods A retrospective case-control study was conducted to analyze the clinical information of 280 patients with unilateral PTC at stages T1–T2 who underwent ipsilateral cervical lateral lymph node dissection and were diagnosed postoperatively via pathological examination in the Department of Thyroid Surgery (General Surgery) at the First Hospital of Shanxi Medical University from February 2019 to February 2024. The patients were randomly allocated into a training set and a test set in a 7∶3 ratio. The general, clinical, laboratory, and imaging data were collected for all patients. The univariate and multivariate logistic regression analyses were used to compare the differences in data between the patients with and without LLNM in the training set. Then the risk factor affecting on the LLNM were used to construct a nomogram prediction model. The receiver operating characteristic (ROC) curve was generated for both the training and test sets, and the area under the ROC curve (AUC) was calculated to evaluate model discrimination. The calibration curve was used to assess model calibration, and decision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram prediction model. Results A total of 280 patients were included, including 196 in the training set and 84 in the testing set. There were no statistically significant differences in clinical and pathological data between the training set and the testing set (P>0.05). There were 147 cases of LLNM in the training set and 63 cases of LLNM in the testing set. The results of multivariate logistic regression analysis showed that the patients with T1–T2 stage unilateral PTC who were male in gender, had cancer lesions located in the upper pole, had central lymph node metastasis, and had larger cancer lesions and higher serum thyroid stimulating hormone level had a higher probability of developing ipsilateral cervical LLNM (P<0.05). The AUC (95% confidence interval) of the nomogram prediction model constructed based on these independent risk factors in the training and testing sets were 0.822 (0.747, 0.897) and 0.838 (0.743, 0.933), respectively. The calibration curves of the training and testing sets roughly overlapped with the reference curve. The DCA results indicated that the net benefit for patients was positive when the threshold probabilities were within the ranges of 15% to 92% for the training set and 10% to 100% for the test set. Conclusions The results of this study suggest that gender, maximum tumor diameter, tumor location, central lymph node metastasis, and serum thyroid stimulating hormone are risk factors affecting the occurrence of ipsilateral cervical LLNM in T1–T2 stages unilateral PTC. The nomogram prediction model constructed based on this demonstrates good discrimination, accuracy, and clinical applicability for its prediction.

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