Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Front Endocrinol (Lausanne). 2024 Aug 20;15:1425101. doi: 10.3389/fendo.2024.1425101. eCollection 2024.
To develop a predictive model using machine learning for levothyroxine (L-T4) dose selection in patients with differentiated thyroid cancer (DTC) after resection and radioactive iodine (RAI) therapy and to prospectively validate the accuracy of the model in two institutions.
A total of 266 DTC patients who received RAI therapy after thyroidectomy and achieved target thyroid stimulating hormone (TSH) level were included in this retrospective study. Sixteen clinical and biochemical characteristics that could potentially influence the L-T4 dose were collected; Significant features correlated with L-T4 dose were selected using machine learning random forest method, and a total of eight regression models were established to assess their performance in prediction of L-T4 dose after RAI therapy; The optimal model was validated through a two-center prospective study (n=263).
Six significant clinical and biochemical features were selected, including body surface area (BSA), weight, hemoglobin (HB), height, body mass index (BMI), and age. Cross-validation showed that the support vector regression (SVR) model was with the highest accuracy (53.4%) for prediction of L-T4 dose among the established eight models. In the two-center prospective validation study, a total of 263 patients were included. The TSH targeting rate based on constructed SVR model were dramatically higher than that based on empirical administration (Rate 1 (first rate): 52.09% (137/263) vs 10.53% (28/266); Rate 2 (cumulative rate): 85.55% (225/263) vs 53.38% (142/266)). Furthermore, the model significantly shortens the time (days) to achieve target TSH level (62.61 ± 58.78 vs 115.50 ± 71.40).
The constructed SVR model can effectively predict the L-T4 dose for postoperative DTC after RAI therapy, thus shortening the time to achieve TSH target level and improving the quality of life for DTC patients.
利用机器学习建立预测模型，以预测分化型甲状腺癌（DTC）患者在甲状腺切除和放射性碘（RAI）治疗后左旋甲状腺素（L-T4）剂量的选择，并前瞻性验证该模型在两个机构的准确性。
本回顾性研究纳入了 266 例甲状腺切除术后接受 RAI 治疗并达到目标促甲状腺激素（TSH）水平的 DTC 患者。收集了 16 个可能影响 L-T4 剂量的临床和生化特征；采用机器学习随机森林方法选择与 L-T4 剂量相关的显著特征，并建立了 8 个回归模型来评估它们在预测 RAI 治疗后 L-T4 剂量的性能；通过两个中心前瞻性研究（n=263）验证最佳模型。
选择了 6 个显著的临床和生化特征，包括体表面积（BSA）、体重、血红蛋白（HB）、身高、体重指数（BMI）和年龄。交叉验证显示，在建立的 8 个模型中，支持向量回归（SVR）模型的预测 L-T4 剂量准确性最高（53.4%）。在两个中心前瞻性验证研究中，共纳入 263 例患者。基于构建的 SVR 模型的 TSH 靶向率显著高于基于经验给药的靶向率（第 1 率（首次率）：52.09%（137/263）比 10.53%（28/266）；第 2 率（累积率）：85.55%（225/263）比 53.38%（142/266））。此外，该模型还显著缩短了达到目标 TSH 水平的时间（天）（62.61 ± 58.78 比 115.50 ± 71.40）。
构建的 SVR 模型可有效预测 RAI 治疗后 DTC 术后的 L-T4 剂量，从而缩短达到 TSH 目标水平的时间，提高 DTC 患者的生活质量。
