Construction and validation of a nomogram model for predicting response to initial radioiodine therapy in differentiated thyroid cancer patients using serum cytokines combined with serum thyroglobulin.

Aim: To investigate the predictive value of serum cytokines for treatment response to initial radioactive iodine therapy (RAI) in patients with intermediate- or high-risk differentiated thyroid cancer (DTC), and to develop a novel nomogram model for this purpose. Materials and methods: This study conducted a retrospective analysis of 429 patients with intermediate- or high-risk DTC who underwent initial RAI therapy across two medical institutions. The training cohort (n = 300) was derived from the Affiliated Hospital of Qingdao University, while the external validation cohort (n = 129) was recruited from Qingdao Central Hospital, University of Health and Rehabilitation Sciences. Treatment response to initial RAI was assessed based on follow-up data collected 6 to 12 months post-treatment. Patients were categorized into excellent response (ER) and non-excellent response (non-ER) groups in accordance with the 2025 American Thyroid Association (ATA) Response Evaluation System. Univariate and multivariate logistic regression analyses were performed within the training cohort to identify independent predictors of treatment response, followed by the development of a nomogram model. The model was subsequently subjected to external validation using calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve analysis. Results: In the training cohort, serum thyroglobulin (sTg) (OR = 0.938, P < 0.001) and serum interferon-alpha (IFN-α) (OR = 1.446, P < 0.001) were identified as independent risk factors for treatment response to initial RAI therapy in patients with intermediate- or high-risk DTC. The ROC curve of the nomogram model developed using the training cohort exhibited acceptable predictive performance. Calibration curves revealed a high degree of concordance between predicted probabilities and observed outcomes. DCA confirmed the potential clinical utility of the model. These findings were further corroborated in the external validation cohort. Conclusions: Based on systematic modeling and external validation, this study integrated sTg and IFN-α to develop and externally validate a novel nomogram for predicting treatment response to initial RAI therapy in patients with intermediate- or high-risk DTC, thereby offering evidence-based support for individualized risk stratification and follow-up planning.