ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Cancer Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1551108
This article is part of the Research TopicClinical prediction models in cancer through bioinformaticsView all 9 articles
Construction and validation of a predictive model for lymph node metastasis in patients with papillary thyroid carcinoma
Provisionally accepted
- 1First Hospital of Shanxi Medical University, Taiyuan, China
- 2Shanxi Medical University, Taiyuan, Shanxi Province, China
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To study the occurrence of lymph node metastasis in patients with papillary thyroid carcinoma (PTC) and construct a predictive model to assess its predictive performance. Methods We retrospectively analyzed the data of 432 patients with PTC.The least absolute shrinkage and selection operator (LASSO) was used to select the features, and multiple logistic regression was used to analyze the predictive factors.Multiple machine learning (ML) classification models are integrated to analyze and identify the optimal model, while Shapley additive exPlanations (SHAPs) are used for personalized risk assessment. A total of 125 patients from Changzhi Heping Hospital were included in an external validation set to evaluate the generalizability of our model.Results: Predictors of central lymph node metastasis (CLNM) included age, sex, maximum nodule diameter, margin, morphology, number of nodules, relationship between the nodule and the thyroid envelope, and coarse calcification. A logistic classification model was identified as the optimal model, with a test set area under the curve (AUC) value of 0.798. The validation results using external data were consistent, demonstrating the stability and generalizability of our model. Conclusion We established a logistic model using the SHAP method, which provides evidence for the ability of the SHAP method to predict lymph node metastasis and serves as a basis for personalized healthcare.
Keywords: Papillary thyroid cancer, lymphonodi cervicales metastasis, machine learning, Prediction model, Shap
Received: 24 Dec 2024; Accepted: 26 May 2025.
Copyright: © 2025 Hao, Zhang, Su and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Li-ping Liu, First Hospital of Shanxi Medical University, Taiyuan, China
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