Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning

Abstract

Objective. If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning (ML) methods in which clinicopathological parameters are included in patients with DTC without distant metastasis. Methods. Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models. Results. After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%). Conclusions. ML models may be used to predict ER in patients with DTC without distant metastasis.