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Updated: Jul 9, 2025
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
A prediction model for identifying high-risk lymph node metastasis in clinical low-risk papillary thyroid
Hui Huang1, Yunhe Liu1, Song Ni2
1Department of Head and Neck Surgical Oncology, National Cancer Centre, National Clinical Research Centre for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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.
