Abstract：
OBJECTIVE: An accurate prognostic assessment is pivotal to adequately inform and individualize follow-up and management of patients with differentiated thyroid cancer (DTC). We aimed to develop a predictive model for recurrent disease in DTC patients treated by surgery and 131I by adopting a decision tree model.
METHODS: Age, sex, histology, T stage, N stage, risk classes, remnant estimation, thyroid-stimulating hormone (TSH), thyroglobulin (Tg), administered 131I activities and post-therapy whole body scintigraphy (PT-WBS) were identified as potential predictors and put into regression algorithm (conditional inference tree, c-tree) to develop a risk stratification model for predicting persistent/recurrent disease over time.
RESULTS: The PT-WBS pattern identified a partition of the population into two subgroups (PT-WBS positive or negative for distant metastases). Patients with distant metastases exhibited lower disease-free survival (either structural, DFS-SD, and biochemical, DFS-BD, disease) compared to those without metastases. Meanwhile, the latter were further stratified into three risk subgroups based on their Tg values. Notably, Tg values >63.1 ng/mL predicted a shorter survival time, with increased DFS-SD for Tg values <63.1 and <8.9 ng/mL, respectively. A comparable model was generated for biochemical disease (BD), albeit different DFS were predicted by slightly different Tg cutoff values (41.2 and 8.8 ng/mL) compared to DFS-SD.
CONCLUSIONS: We developed a simple, accurate and reproducible decision tree model able to provide reliable information on the probability of structurally and/or biochemically persistent/relapsed DTC after a TTA. In turn, the provided information is highly relevant to refine the initial risk stratification, identify patients at higher risk of reduced structural and biochemical DFS, and modulate additional therapies and the relative follow-up.
METHODS: Age, sex, histology, T stage, N stage, risk classes, remnant estimation, thyroid-stimulating hormone (TSH), thyroglobulin (Tg), administered 131I activities and post-therapy whole body scintigraphy (PT-WBS) were identified as potential predictors and put into regression algorithm (conditional inference tree, c-tree) to develop a risk stratification model for predicting persistent/recurrent disease over time.
RESULTS: The PT-WBS pattern identified a partition of the population into two subgroups (PT-WBS positive or negative for distant metastases). Patients with distant metastases exhibited lower disease-free survival (either structural, DFS-SD, and biochemical, DFS-BD, disease) compared to those without metastases. Meanwhile, the latter were further stratified into three risk subgroups based on their Tg values. Notably, Tg values >63.1 ng/mL predicted a shorter survival time, with increased DFS-SD for Tg values <63.1 and <8.9 ng/mL, respectively. A comparable model was generated for biochemical disease (BD), albeit different DFS were predicted by slightly different Tg cutoff values (41.2 and 8.8 ng/mL) compared to DFS-SD.
CONCLUSIONS: We developed a simple, accurate and reproducible decision tree model able to provide reliable information on the probability of structurally and/or biochemically persistent/relapsed DTC after a TTA. In turn, the provided information is highly relevant to refine the initial risk stratification, identify patients at higher risk of reduced structural and biochemical DFS, and modulate additional therapies and the relative follow-up.
摘要：
目的：准确的预后评估对于充分告知分化型甲状腺癌（DTC）患者的个体化随访和管理至关重要。我们旨在通过采用决策树模型来开发接受手术和131I治疗的DTC患者复发性疾病的预测模型。
方法：年龄，性别,组织学,T级,N级,风险类别，剩余估计，促甲状腺激素(TSH),甲状腺球蛋白（Tg），给予131I活动和治疗后全身闪烁显像（PT-WBS）被确定为潜在的预测因子，并放入回归算法（条件推断树，c-tree）来开发风险分层模型，以预测随时间的持续/复发疾病。
结果：PT-WBS模式将人群分为两个亚组（PT-WBS远处转移阳性或阴性）。远处转移的患者表现出较低的无病生存率(无论是结构性的，DFS-SD,和生化，DFS-BD,疾病)与没有转移的患者相比。同时,后者根据Tg值进一步分为三个风险亚组.值得注意的是,Tg值>63.1ng/mL预测存活时间较短，Tg值<63.1和<8.9ng/mL时DFS-SD增加，分别。针对生化疾病（BD）生成了一个可比较的模型，尽管通过与DFS-SD相比略微不同的Tg截止值（41.2和8.8ng/mL）来预测不同的DFS。
结论：我们开发了一种简单的，准确且可重复的决策树模型，能够提供有关TTA后结构和/或生化持续/复发DTC概率的可靠信息。反过来,提供的信息与完善初始风险分层高度相关，确定结构和生化DFS降低风险较高的患者，并调整其他疗法和相对随访。
方法：年龄，性别,组织学,T级,N级,风险类别，剩余估计，促甲状腺激素(TSH),甲状腺球蛋白（Tg），给予131I活动和治疗后全身闪烁显像（PT-WBS）被确定为潜在的预测因子，并放入回归算法（条件推断树，c-tree）来开发风险分层模型，以预测随时间的持续/复发疾病。
结果：PT-WBS模式将人群分为两个亚组（PT-WBS远处转移阳性或阴性）。远处转移的患者表现出较低的无病生存率(无论是结构性的，DFS-SD,和生化，DFS-BD,疾病)与没有转移的患者相比。同时,后者根据Tg值进一步分为三个风险亚组.值得注意的是,Tg值>63.1ng/mL预测存活时间较短，Tg值<63.1和<8.9ng/mL时DFS-SD增加，分别。针对生化疾病（BD）生成了一个可比较的模型，尽管通过与DFS-SD相比略微不同的Tg截止值（41.2和8.8ng/mL）来预测不同的DFS。
结论：我们开发了一种简单的，准确且可重复的决策树模型，能够提供有关TTA后结构和/或生化持续/复发DTC概率的可靠信息。反过来,提供的信息与完善初始风险分层高度相关，确定结构和生化DFS降低风险较高的患者，并调整其他疗法和相对随访。
