DataSheet_1_A user-friendly nomogram for predicting radioiodine refractory differentiated thyroid cancer.docx

BackgroundThe diagnosis of radioiodine refractory differentiated thyroid cancer (RAIR-DTC) is primarily based on clinical evolution and iodine uptake over the lesions, which is still time-consuming, thus urging a predictive model for timely RAIR-DTC informing. The aim of this study was to develop a nomogram model for RAIR prediction among DTC patients with distant metastases (DM).
MethodsData were extracted from the treatment and follow-up databases of Peking Union Medical College Hospital between 2010 and 2021. A total of 124 patients were included and divided into RAIR (n=71) and non-RAIR (n=53) according to 2015 ATA guidelines. All patients underwent total thyroidectomy followed by at least two courses of RAI treatment. Serological markers and various clinical, pathological, genetic status, and imaging factors were integrated into this study. The pre-treatment stimulated Tg and pre- and post-treatment suppressed Tg at the first and second course RAI treatment were defined as s-Tg1, s-Tg2, sup-Tg1, and sup-Tg2, respectively. Δs-Tg denoted s-Tg1/s-Tg2, and Δs-TSH denoted s-TSH1/s-TSH2. Multivariate logistic regression and correlation analysis were utilized to determine the independent predictors of RAIR. The performance of the nomogram was assessed by internal validation and receiver operating characteristic (ROC) curve, and benefit in clinical decision-making was assessed using decision curve.
ResultsIn univariate logistic regression, nine possible risk factors were related to RAIR. Correlation analysis showed four of the above factors associated with RAIR. Through multivariate logistic regression, Δs-Tg/Δs-TSH<1.50 and age upon diagnosis were obtained to develop a convenient nomogram model for predicting RAIR. The model was internally validated and had good predictive efficacy with an AUC of 0.830, specificity of 0.830, and sensitivity of 0.755. The decision curve also showed that if the model is used for clinical decision-making when the probability threshold is between 0.23 and 0.97, the net benefit of patients is markedly higher than that of the TreatAll and TreatNone control groups.
By using 1.50 as a cut-off ofΔs-Tg/Δs-TSH, differing biochemical progression among the generally so-called RAIR can be further stratified as meaningfully rapidly or slowly progressive patients (P=0.012).
ConclusionsA convenient user-friendly nomogram model was developed with good predictive efficacy for RAIR. The progression of RAIR can be further stratified as rapidly or slowly progressive by using 1.50 as a cut-off value of Δs-Tg/Δs-TSH.

背景：放射性碘难治性分化型甲状腺癌（radioiodine refractory differentiated thyroid cancer, RAIR-DTC）的诊断主要基于临床病程及病灶摄碘情况，目前该诊断流程仍较为耗时，因此亟需一款可及时提示RAIR-DTC发生的预测模型。本研究旨在构建一款针对伴远处转移（distant metastases, DM）的分化型甲状腺癌患者的RAIR预测列线图模型。
方法：本研究数据提取自北京协和医院2010至2021年的治疗与随访数据库。最终纳入124例患者，依据2015年美国甲状腺协会（ATA）指南将其分为RAIR组（n=71）与非RAIR组（n=53）。所有患者均接受甲状腺全切术，且后续至少接受2个疗程的放射性碘（RAI）治疗。本研究整合了血清学标志物、多项临床、病理、遗传学特征及影像学因素。将首次及第二次RAI疗程的治疗前刺激性Tg，以及各疗程治疗前与治疗后的抑制性Tg分别定义为s-Tg1、s-Tg2、sup-Tg1及sup-Tg2。其中Δs-Tg指代s-Tg1与s-Tg2的比值，Δs-TSH指代s-TSH1与s-TSH2的比值。本研究采用多因素logistic回归与相关性分析确定RAIR的独立预测因子。通过内部验证及受试者工作特征（ROC）曲线评估列线图的预测性能，并采用决策曲线分析评估其在临床决策中的获益价值。
结果：单因素logistic回归分析显示，共9项潜在风险因素与RAIR相关。相关性分析进一步筛选出其中4项与RAIR存在关联。通过多因素logistic回归分析，最终确定Δs-Tg/Δs-TSH<1.50及诊断时年龄为RAIR的独立预测因子，并据此构建了一款便捷的RAIR预测列线图模型。该模型经内部验证后展现出良好的预测效能，其曲线下面积（AUC）为0.830，特异度为0.830，灵敏度为0.755。决策曲线分析结果显示，当概率阈值介于0.23至0.97之间时，采用该模型指导临床决策可为患者带来显著高于“全部治疗”与“全部不治疗”对照组的净获益。以1.50作为Δs-Tg/Δs-TSH的截断值，可将通常意义上的RAIR患者进一步分层为进展快速与进展缓慢的亚组（P=0.012）。
结论：本研究构建了一款便捷易用的列线图模型，其对RAIR具备良好的预测效能。以1.50作为Δs-Tg/Δs-TSH的截断值，可进一步将RAIR患者的疾病进展分层为快速进展与缓慢进展两类。