Abstract
PURPOSE: Current guidelines provide a recognized yet broad framework for stratifying recurrence risk in differentiated thyroid cancer (DTC) patients. More precise tools are needed for intermediate- and high-risk groups. This study aims to identify recurrence-associated risk factors and develop a machine learning-based predictive model. METHODS: In this retrospective analysis, 2,388 DTC patients were randomly assigned to a training group (1,910 cases) and a validation group (478 cases). Predictive factors were identified using univariate and multivariate analyses. Six machine learning models were trained and validated, with performance evaluated through accuracy, area under the curve, and clinical utility via decision curve analysis. RESULTS: Independent risk factors for recurrence included intraglandular dissemination, total tumor size, bilateral cervical lymph node involvement, and Hashimoto's thyroiditis, while normal/elevated TSH and multifocal nodules were protective. The random forest model demonstrated the best performance (training accuracy: 0.801; validation accuracy: 0.808). A random forest-based online calculator was developed to facilitate individualized risk assessment in clinical settings. CONCLUSIONS: The random forest model effectively predicts DTC recurrence, offering a practical tool for individualized risk assessment and aiding clinical decision-making.