Analysis of risk factors for lymph node metastasis in 241 patients with thyroid carcinoma and establishment of a prediction model

This study aimed to identify risk factors for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) and develop a clinical prediction model. Retrospectively, data were collected from 348 PTC patients treated at the Second Affiliated Hospital of Nanchang University between January 2019 and December 2022, with 241 patients included in the final analyses. Patients with lateral cervical LNM were categorized into a metastasis group, and those without were in a non -metastasis group. The patients were divided into a training set (n=169) and a validation set (n=72) in a 7:3 ratio. Logistic and least absolute shrinkage and selection operator (LASSO) regression models were used to identify key factors associated with lateral cervical LNM and prognosis, enabling the construction of a predictive model. The model's validity was assessed via the Hosmer-Lemeshow Test, calibration curves, ROC curves, and decision curve analysis. The metastasis group exhibited higher proportions of males, multiple lesions, bilateral involvement, tumor diameter >= 1 cm, and elevated levels of PLR, LMR, and NLR (P<0.05). Logistic regression analysis revealed that gender, multiple lesions, affected side, and tumor diameter were associated with lateral cervical LNM (P<0.05). The predictive Nomogram model, which included factors like affected side, tumor diameter, capsular invasion, central LNM, PLR, and NLR, demonstrated strong predictive accuracy and clinical utility. Thus, this study provides a practical clinical tool through an accurate Nomogram model to assess lateral cervical LNM risk in PTC patients using logistic and LASSO regression analyses.