A predictive model and survival analysis for local recurrence in differentiated thyroid carcinoma

Local recurrence (LR) is associated with poor outcome in patients with differentiated thyroid carcinoma (DTC). The aim of this study was to explore potential risk factors for LR and build a predictive model.The medical data of patients who were diagnosed with DTC after initial surgery in three medical centers (2000-2018) were reviewed. Detailed clinicopathologic characteristics of all cases were identified.Multiple factors, including extrathyroidal extension (ETE), histology, symptoms, multifocality, and tumor diameter, were significantly different between the LR and no evidence of disease groups in univariate and multivariate analysis (P˂0.05). Tumor diameter, symptoms, and ETE made the greatest contributions to prognosis according to decision tree analysis and random forest algorithm. The predictive model constructed from these data achieved 98.7% accuracy of classification. A five-fold cross-validation confirmed that the model has 84.7-89.7% accuracy of classification. Additionally, symptoms and ETE were independent predictors on survival analysis (P˂0.05).This study optimized the weight of risk factors, including tumor diameter, symptoms, ETE, and multifocality, in predicting LR in patients with DTC. Our predictive model provides a strong tool to distinguish between high-risk and low-risk DTC.