我们旨在基于治疗前的信息，开发一种用于预测不伴有结构性疾病的分化型甲状腺癌（DTC）患者在131I放射性碘治疗和TSH抑制治疗中的治疗反应的机器学习模型。总体上，将597名和326名无结构性疾病的DTC患者随机分配到对131I治疗和TSH抑制治疗的治疗反应进行预测的“训练”队列中。使用了六种有监督算法，包括逻辑回归、支持向量机、随机森林（RF）、神经网络、自适应增强和梯度提升，预测对131I治疗的有效反应（ER）和对TSH抑制治疗的生化缓解（BR）。刺激和抑制甲状腺球蛋白（Tg）以及当前131I治疗开始前的放射性碘摄取主要归因于对131I治疗的有效反应，而最后一次131I治疗后全身扫描上可见的甲状腺残余物和TSH则对TSH抑制治疗中的Tg下降起到了很大的贡献。RF模型在所有模型中表现最佳。RF用于在131I治疗期间将ER与非ER进行区分的准确率和受试者工作特征曲线下面积（AUC）分别为81.3%和0.896。RF用于预测TSH抑制治疗的BR的准确率和AUC分别为78.7%和0.857。本研究表明，机器学习模型，尤其是RF算法，是一种有用的工具，可以基于治疗前的常规临床变量和生化标志物预测无结构性疾病的DTC患者对131I治疗和TSH抑制治疗的治疗反应。版权所有© 2023 作者 发表于牛津大学出版社。

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.© The Author(s) 2023. Published by Oxford University Press.