Author(s)

Zhiyue Tang, Hua Pang^*

Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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.

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

Tang, Z.Y. and Pang, H. (2026) Research Progress on Efficacy Prediction of Radioiodine Therapy in Differentiated Thyroid Cancer. Journal of Biosciences and Medicines, 14, 395-407. doi: 10.4236/jbm.2026.144029.