Random Forest for Predicting Treatment Response to Radioiodine and Thyrotropin Suppression Therapy in Patients With Differentiated Thyroid Cancer But Without Structural Disease.

Abstract

We aimed to develop a machine-learning model for predicting treatment response to radioiodine (131I) therapy and thyrotropin (TSH) suppression therapy in patients with differentiated thyroid cancer (DTC) but without structural disease, based on pre-treatment information. Overall, 597 and 326 patients with DTC but without structural disease were randomly assigned to "training" cohorts for predicting treatment response to 131I therapy and TSH suppression therapy, respectively. Six supervised algorithms, including Logistic Regression, Support Vector Machine, Random Forest (RF), Neural Networks, Adaptive Boosting, and Gradient Boost, were used to predict effective response (ER) to 131I therapy and biochemical remission (BR) to TSH suppression therapy. Stimulated and suppressed thyroglobulin (Tg) and radioiodine uptake before the current course of 131I therapy were mostly attributed to ER to 131I therapy, while thyroid remnant available on the post-therapeutic whole-body scan at the last course of 131I therapy and TSH were greatly contributed to Tg decline under TSH suppression therapy. RF showed the best performance among all models. The accuracy and area under the receiver operating characteristic curve (AUC) for segregating ER from non-ER during 131I therapy with RF were 81.3% and 0.896, respectively. The accuracy and AUC for predicting BR to TSH suppression therapy with RF were 78.7% and 0.857, respectively. This study demonstrates that machine learning models, especially the RF algorithm are useful tools that may predict treatment response to 131I therapy and TSH suppression therapy in DTC patients without structural disease based on pre-treatment routine clinical variables and biochemical markers.