Comparison of Models Predicting Efficacy of Radioiodine Therapy in Patients with Differentiated Thyroid Cancer

Purpose Differentiated thyroid cancer (DTC) is the most common type of endocrine malignancy, with its incidence on the rise over recent decades. Despite a favorable prognosis, DTC management remains complex, often involving thyroidectomy followed by radioactive iodine (RAI) therapy. While RAI is crucial for patient outcomes, its efficacy varies, necessitating the identification of predictors for treatment response. New guidelines underscore the need for personalized follow-up plans, prompting research into predictive models to refine prognostic accuracy. Methods We conducted a retrospective analysis of 744 DTC patients treated at a single center, focusing on clinicopathological factors and thyroid biomarkers. Multivariate logistic regression models were constructed to evaluate the predictive value of different DTC biomarkers, adjusting for covariates such as age, sex, and disease stage. Cut-off values for these biomarkers were determined to predict RAI efficacy. Results Analysis revealed no significant difference in predictive performance among models incorporating various DTC biomarkers. Stimulated thyroglobulin (sTg) emerged as a reliable predictor, with a mean cut-off value of 7.22 ng/mL. Additionally, chronic lymphocytic thyroiditis (CLT) status tended to enhance predictive accuracy, although not significantly. Conclusions Our study underscores the utility of sTg as a single parameter for predicting RAI efficacy in DTC patients, with a defined cut-off value facilitating clinical decision-making. The inclusion of CLT status may further enhance predictive models, warranting consideration in future analyses. Overall, our findings contribute to the advancement of personalized management approaches for DTC patients undergoing RAI therapy.