To Develop and Validate a Nomogram Model for Predicting High Volume (＞5) Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma

Objectives To develop a nomogram model for predicting the risk of high volume (>5) of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma and to evaluate the effectiveness of the model in clinical application, in order to achieve the goal of initial risk stratification of patients with PTMC, individualized design of the scope of surgery, and reduction the incidence of secondary surgery in patients with PTMC by the clinicians. Methods Retrospective analysis of clinical characteristics of patients with PTMC in the Surveillance, Epidemiology, and End Results (SEER) database between January 2016 and December 2020 and the clinical case data of patients who presented to the Gland Surgery, Hebei General Hospital, underwent surgical treatment, and were ultimately pathologically diagnosed with PTMC between January 2021 and December 2023.The clinicopathological characteristics included in the training group were screened using univariate and multivariate logistic regression analyses, to determine the independent risk factors for high volume CLNM in patients with PTMC, and to construct a nomogram model for predicting high volume CLNM. Results The male gender, lager tumor size(>5mm), multifocality, and extra-thyroidal invasion were independent risk factors for high volume CLNM in patients with papillary thyroid microcarcinoma. In contrast, elderly age(≥ 55years) at diagnosis was identified as a protective factor.Based on these independent risk factors, a nomogram model was further constructed to predict high volume CLNM. Conclusions 1 Male, large tumor diameter (>5mm), multifocal, and extra-thyroidal invasion were independent risk factors for high volume CLNM of patients with papillary thyroid microcarcinoma. In contrast, age ≥ 55 at the time of diagnosis was identified as a protective factor.2 The clinical prediction model based on the above mentioned factors has good predictive value, and provides a better individualized clinical decision for the management of PTMC patients by surgeons.