Development and internal validation of a nomogram for predicting the initial response to radioactive iodine therapy in differentiated thyroid cancer with concurrent Hashimoto's thyroiditis.

BACKGROUND: Differentiated thyroid cancer (DTC) frequently coexists with Hashimoto's thyroiditis (HT), and HT may influence the response to radioactive iodine (RAI) therapy. This study aims to establish and validate a clinical predictive model for the response to initial RAI therapy in patients with DTC complicated by HT, thereby providing a personalized tool for optimizing clinical decision-making. METHODS: A total of 461 patients with DTC and HT were included in the study. Variable selection was conducted using least absolute shrinkage and selection operator (LASSO) regression, followed by the construction of a predictive nomogram based on multivariable binary logistic regression. Model performance was evaluated through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS: Among the 461 patients, 235 achieved an excellent response (ER), while 226 experienced a non-ER (NER). Utilizing five predictors identified by LASSO-N stage, tumor multifocality, lymph node metastasis rate, stimulated thyroglobulin (sTg), and thyroglobulin antibody (TgAb)-a binary logistic regression model and corresponding nomogram were developed. The model demonstrated an area under the ROC curve (AUC) of 0.864 in the training set and 0.882 in the validation set for predicting NER. Moreover, calibration curve analysis and DCA indicated acceptable agreement between predicted and observed outcomes, as well as potential clinical net benefit across a range of threshold probabilities, suggesting that the model may be useful for individualized risk stratification. CONCLUSIONS: This study successfully developed and internally validated a clinical prediction model for assessing the response to initial RAI therapy in patients with DTC complicated by HT. The model may serve as a reliable reference for personalized clinical decision-making.