DTC患者^(131)I治疗后左甲状腺素钠片最佳初始剂量预测模型探究

Exploration of the prediction model for the optimal initial dose of levothyroxine sodium tablets in patients with DTC after^(131)I treatment

目的应用机器学习方法为^(131)I治疗后的分化型甲状腺癌(DTC)患者构建左甲状腺素钠片最佳初始剂量的预测模型。方法回顾性分析2019年11月至2020年11月在天津市肿瘤医院空港医院接受^(131)I治疗后促甲状腺激素(TSH)抑制治疗最终达标的266例DTC患者[男性78例(男性组)、女性188例(女性组),年龄18~70(40.0+11.5)岁]的临床资料,包括一般资料、生化指标(共16项)和出院后定期复查的甲状腺功能相关数据及左甲状腺素钠片每次的调整剂量。通过计算随机森林模型重要度筛选出较为重要的临床特征;以筛选出的特征为自变量,以左甲状腺素钠片达标剂量为因变量构建多种回归模型,通过交叉验证选出准确率最高的模型。计数资料的组间比较采用独立样本的卡方检验。结果266例患者的体重、身高、体重指数、体表面积、血红蛋白、平均红细胞体积、收缩压/舒张压、术后甲状旁腺激素、达标时左甲状腺素钠片剂量分别为(68.4±12.9)kg、(165.8±12.8)cm、24.6±3.5、(1.9±0.2)m^(2)、(140.1±19.1)g/L、(88.6±5.5)fl、(125.7±18.9)mm Hg/(82.7±12.4)mm Hg、(4.1±2.2)pmol/L、(117.0±30.1)μg/d。通过特征筛选,重要度排在前6位的临床特征依次为体表面积、体重、血红蛋白、身高、体重指数、年龄,其重要度均数依次为0.2805、0.1951、0.^(131)5、0.1252、0.1080、0.0819。径向基核的支持向量回归(SVR)模型预测左甲状腺素钠片剂量的准确率(53.4%,142/266)最高,高于经验给药的首次达标率(15.0%,40/266);SVR模型在女性组中预测左甲状腺素钠片剂量的准确率高于男性组[60.6%(114/188)对35.9%(28/78)],且差异有统计学意义(χ^(2)=13.51,P<0.001)。结论基于机器学习构建的SVR模型有望提高经^(131)I治疗后的DTC患者左甲状腺素钠片的首次达标率,并且在女性患者中更有效。

Objective To construct a prediction model for the optimal initial dose of levothyroxine sodium tablets in patients with differentiated thyroid cancer(DTC)after^(131)I treatment by machine learning.Methods A total of 266 DTC patients(78 males(male group)and 188 females(female group),aged 18 to 70(40.0+11.5)years old)who received^(131)I treatment followed by thyroid stimulating hormone(TSH)suppressive therapy in the Department of Nuclear Medicine,Konggang Hospital,Tianjin Cancer Hospital between November 2019 and November 2020 were retrospectively analyzed for final compliance.A total of 16 clinical and biochemical indicators and data related to thyroid function were obtained,and each adjusted dose of levothyroxine sodium tablets was collected from patients with regular post-discharge rechecks.The indicators strongly correlated with the optimal dose of levothyroxine sodium tablets were screened by calculating random forest feature importance.A wide variety of regression models were constructed with the selected indicators and optimal dose of levothyroxine sodium tablets as independent and dependent variables,respectively.Selected the most accurate model using the cross-validation method.Counting data were compared between male and female groups using the chi-square test of independence.Results Body weight,height,body mass index,body surface area,hemoglobin,mean corpuscular volume,systolic/diastolic blood pressure,postoperative parathyroid hormone,and the reaching levothyroxine sodium tablets dose of 266 patients were(68.4±12.9)kg,(165.8±12.8)cm,24.6±3.5,(1.9±0.2)m^(2),(140.1±19.1)g/L,(88.6±5.5)fl,(125.7±18.9)mm Hg/(82.7±12.4)mm Hg,(4.1±2.2)pmol/L,and(117.0±30.1)μg/d,respectively.Six indicators with a strong correlation with levothyroxine sodium tablets dose were screened using the feature selection method.According to the order of importance,the six indicators were body surface area,body weight,hemoglobin,height,body mass index,and age.Their average random forest importances were 0.2805,0.1951,0.^(131)5,0.1252,0.1080 and 0.0819 respectively.The support vector regression(SVR)model using radial basis kernel had the highest accuracy(53.4%,142/266)by cross-training validation.In addition,in this study,SVR's accuracy was significantly higher than the first success rate of empirical administration of levothyroxine sodium tablets(15.0%,40/266).Moreover,the SVR model's accuracy was compared by dividing the patients into different subgroups according to gender.The results showed that the female patient group's accuracy was significantly higher than that of the male group(60.6%(114/188)vs.35.9%(28/78)),with a statistically significant difference(χ^(2)=13.51,P<0.001).Conclusions The SVR model is constructed based on machine learning and is expected to improve the first success rate of levothyroxine sodium tablets in DTC patients after being treated with^(131)I.It is more pronounced in female patients and helps to improve the quality of life and prognosis among DTC patients.