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Hou, F., Zhu, Y., Zhao, H., Cai, H., Wang, Y., Peng, X., et al. (2024) Development and Validation of an Interpretable Machine Learning Model for Predicting the Risk of Distant Metastasis in Papillary Thyroid Cancer: A Multicenter Study. eClinicalMedicine, 77, Article ID: 102913.
https://doi.org/10.1016/j.eclinm.2024.102913

has been cited by the following article:

• TITLE: Research Progress on Efficacy Prediction of Radioiodine Therapy in Differentiated Thyroid Cancer

  AUTHORS: Zhiyue Tang, Hua Pang

  KEYWORDS: Differentiated Thyroid Cancer, Radioiodine Therapy, Response Prediction, Machine Learning

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.14 No.4, April 30, 2026

  ABSTRACT: Differentiated thyroid cancer represents the most common pathological type of thyroid malignancy, for which postoperative radioiodine therapy serves as a crucial adjuvant treatment modality. However, significant inter-individual heterogeneity exists in the response to radioiodine therapy among differentiated thyroid cancer patients, which current staging systems, such as the tumor-node-metastasis staging and the American Thyroid Association risk stratification system, are insufficient to accurately predict. Currently, research into predicting the response to radioiodine therapy has progressively evolved from the identification of independent predictive factors and the construction of composite parameters, through the development of traditional statistical models, to the application of machine learning algorithms. This article aims to review the aforementioned research progress, critically evaluate the advantages and limitations of existing methods, and thereby provide a reference for the development of precise predictive models and the optimization of individualized treatment strategies.