A prediction model for identifying high-risk lymph node metastasis in clinical low-risk papillary thyroid microcarcinoma

Abstract

Abstract Background The presence of high-volume lymph node metastasis (LNM) and extranodal extension (ENE) greatly increases the risk of recurrence in patients with low-risk papillary thyroid microcarcinoma (PTMC). The goal of this research was to analyze the factors that contribute to high-risk lymph node metastasis in patients with low-risk PTMC. Methods We analyzed the records of 7344 patients who were diagnosed with low-risk PTMC and treated at our center from January 2013 to June 2018.LNM with a high volume or ENE was classified as high-risk lymph node metastasis (hr-LNM). A logistic regression analysis was conducted to identify the risk factors associated with hr-LNM. A nomogram was created and verified using risk factors obtained from LASSO regression analysis, to predict the likelihood of hr-LNM. Results The rate of hr-LNM was 6.5%. LASSO regression revealed six variables that independently contribute to hr-LNM: sex, age, tumor size, tumor location, Hashimoto’s thyroiditis (HT), and microscopic capsular invasion. A predictive nomogram was developed by integrating these risk factors, demonstrating its excellent performance. Upon analyzing the receiver operating characteristic (ROC) curve for predicting hr-LNM, it was observed that the area under the curve (AUC) had a value of 0.745 and 0.730 in the training and testing groups showed strong agreement, affirming great reliability. Conclusion Sex, age, tumor size, tumor location, HT, and microscopic capsular invasion were determined to be key factors associated with hr-LNM in low-risk PTMC. Utilizing these factors, a nomogram was developed to evaluate the risk of hr-LNM in patients with low-risk PTMC.