基于Lasso-Logistic回归分析构建甲状腺癌术后<sup>131</sup>I治疗疗效的预测模型-东南大学学报医学版

基于Lasso-Logistic回归分析构建甲状腺癌术后¹³¹I治疗疗效的预测模型

单位：1. 唐山市人民医院甲乳外科, 河北唐山 067000;
2. 唐山市人民医院护理部, 河北唐山 067000

关键词：甲状腺癌 ¹³¹I治疗 Lasso-Logistic回归分析影响因素预测模型

分类号：R736.1

出版年·卷·期（页码）：2026·45·第四期（612-620）

摘要：

目的:基于Lasso-Logistic回归分析筛选甲状腺癌术后¹³¹I治疗疗效的影响因素,并构建预测模型。方法:回顾性选取唐山市人民医院2022年9月至2025年2月360例确诊为分化型甲状腺癌(DTC)患者,按7:3将其分为训练集和验证集。所有研究对象术后均接受¹³¹I治疗, 6个月后评估其疗效。收集患者临床资料,经Lasso-Logistic回归分析筛选¹³¹I治疗疗效的影响因素,并构建列线图模型。结果:360例DTC患者中82例患者治疗无效,无效发生率为22.78%。Lasso-Logistic回归分析显示,肿瘤最大直径、肿瘤转移、BRAF V600E 突变、治疗前甲状腺球蛋白(Tg)水平均为患者术后¹³¹I 治疗无效的独立危险因素(P＜0.05),¹³¹I治疗剂量为其保护因素(P＜0.05);基于此构建列线图预测模型,绘制ROC曲线,结果显示,训练集和验证集中模型预测患者治疗无效的曲线下面积(AUC)分别为0.885(95%CI:0.822~0.936)、0.823(95%CI:0.786~0.892),表明该模型具有较好的区分度;采用Bootstrap法进行内部验证(重复抽样1 000次),平均绝对误差为0.011;Hosmer-Lemeshow拟合优度检验结果显示,训练集和验证集中 χ²值分别为1.525、1.286,均P＞0.05;校准曲线显示,预测概率与实际概率基本一致;决策曲线显示,阈概率在0.1~0.9范围内,模型均具有较好的临床净收益。结论:肿瘤最大直径、肿瘤转移、BRAF V600E 突变、¹³¹I治疗剂量、治疗前Tg水平均为患者¹³¹I治疗疗效的影响因素,基于此构建的列线图模型可用于准确预测患者¹³¹I治疗无效风险。

Objective: To screen the influencing factors of ¹³¹I treatment response in patients with differentiated thyroid cancer(DTC) and construct a predictive nomogram model based on Lasso-Logistic regression. Methods: A total of 360 patients diagnosed with DTC who underwent postoperative ¹³¹I therapy in Tangshan People's Hospital from September 2022 to February 2025 were retrospectively enrolled. All patients were randomly divided into a training set and a validation set. Treatment efficacy was evaluated at 6 months after ¹³¹I therapy, and patients in the training set were categorized into effective and ineffective groups according to therapeutic outcomes. Baseline clinical data were collected, and LASSO-Logistic regression was performed to screen independent factors associated with ¹³¹I treatment failure. A visual nomogram was subsequently established. Results: Among the 360 DTC patients, 82 cases presented with treatment failure, yielding an ineffective rate of 22.78%. LASSO-Logistic regression identified maximum tumor diameter, tumor metastasis, BRAF V600E mutation, and pretreatment thyroglobulin(Tg) level as independent risk factors for postoperative I treatment failure(all P<0.05), while ¹³¹I therapeutic dose served as a protective factor(P<0.05). The receiver operating characteristic(ROC) curves were plotted using model-predicted probability as the predictive variable and actual therapeutic efficacy as the gold standard. The area under the curve(AUC) values for predicting treatment failure were 0.885(95%CI: 0.822-0.936) in the training set and 0.823(95%CI: 0.786-0.892) in the validation set, indicating good discriminatory ability of the model. Internal validation was performed via 1 000-bootstrap resampling with a mean absolute error of 0.011. The Hosmer-Lemeshow test demonstrated favorable goodness-of-fit for both the training set(χ²=1.525, P>0.05) and validation set(χ²=1.286, P>0.05), and calibration curves showed high consistency between predicted and actual probabilities. Decision curve analysis confirmed that the nomogram yielded favorable clinical net benefits within the threshold probability range of 0.1-0.9. Conclusion: Maximum tumor diameter, tumor metastasis, BRAF V600E mutation, pretreatment Tg level, and ¹³¹I therapeutic dose are key factors affecting ¹³¹I treatment efficacy. The nomogram model constructed based on these factors can accurately predict the risk of postoperative ¹³¹I treatment failure in DTC patients.

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