PDF(Pubmed)
Abstract：
UNASSIGNED: Anaplastic thyroid cancer (ATC) is highly invasive, prone to distant metastasis (DM), and has a very poor prognosis. This study aims to construct an accurate survival prediction model for ATC patients with DM, providing reference for comprehensive assessment and treatment planning.
UNASSIGNED: We extracted data of ATC patients with DM diagnosed between 2004 and 2019 from the SEER database, randomly dividing them into a training set and a validation set in a ratio of 7:3. Univariate and multivariate Cox regression analyses were sequentially performed on the training set to identify independent prognostic factors for overall survival (OS) and construct nomograms for 3-month, 6-month, and 8-month OS for ATC patients with DM based on all identified independent prognostic factors. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) curve analysis, and calibration curves were separately plotted on the training and validation sets to demonstrate the model\'s performance. Furthermore, patients were stratified into high- and low-risk groups based on their risk scores, and the Kaplan-Meier (KM) survival curves were used to illustrate the survival differences between the two groups.
UNASSIGNED: A total of 322 patients were included in this study. Univariate and multivariate Cox regression analyses identified five independent prognostic factors for OS in ATC patients with DM: surgery, tumor size, age, chemotherapy, and radiotherapy. Nomograms for 3-month, 6-month, and 8-month OS were established based on these factors. The training set AUC values (3-month AUC: 0.767, 6-month AUC: 0.789, 8-month AUC: 0.795) and validation set AUC values (3-month AUC: 0.753, 6-month AUC: 0.798, 8-month AUC: 0.806) as well as the calibration curves demonstrated excellent applicability and accuracy of the model. Additionally, the DCA curves indicated substantial clinical net benefit of the model. The KM curves also confirmed the model\'s excellent stratification ability for patient OS.
UNASSIGNED: The nomogram developed in this study accurately predicts OS for ATC patients with DM. It can assist clinicians in formulating appropriate treatment strategies for these patients.
UNASSIGNED: We extracted data of ATC patients with DM diagnosed between 2004 and 2019 from the SEER database, randomly dividing them into a training set and a validation set in a ratio of 7:3. Univariate and multivariate Cox regression analyses were sequentially performed on the training set to identify independent prognostic factors for overall survival (OS) and construct nomograms for 3-month, 6-month, and 8-month OS for ATC patients with DM based on all identified independent prognostic factors. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) curve analysis, and calibration curves were separately plotted on the training and validation sets to demonstrate the model\'s performance. Furthermore, patients were stratified into high- and low-risk groups based on their risk scores, and the Kaplan-Meier (KM) survival curves were used to illustrate the survival differences between the two groups.
UNASSIGNED: A total of 322 patients were included in this study. Univariate and multivariate Cox regression analyses identified five independent prognostic factors for OS in ATC patients with DM: surgery, tumor size, age, chemotherapy, and radiotherapy. Nomograms for 3-month, 6-month, and 8-month OS were established based on these factors. The training set AUC values (3-month AUC: 0.767, 6-month AUC: 0.789, 8-month AUC: 0.795) and validation set AUC values (3-month AUC: 0.753, 6-month AUC: 0.798, 8-month AUC: 0.806) as well as the calibration curves demonstrated excellent applicability and accuracy of the model. Additionally, the DCA curves indicated substantial clinical net benefit of the model. The KM curves also confirmed the model\'s excellent stratification ability for patient OS.
UNASSIGNED: The nomogram developed in this study accurately predicts OS for ATC patients with DM. It can assist clinicians in formulating appropriate treatment strategies for these patients.
摘要：
■间变性甲状腺癌（ATC）具有高度侵袭性，容易发生远处转移（DM），预后很差.本研究旨在构建ATC合并DM患者的准确生存预测模型,为综合评估和治疗规划提供参考。
■我们从SEER数据库中提取了2004年至2019年间诊断为DM的ATC患者的数据，以7:3的比例将它们随机分为训练集和验证集。对训练集依次进行单变量和多变量Cox回归分析，以确定总生存期（OS）的独立预后因素，并构建3个月的列线图。6个月,根据所有确定的独立预后因素，ATC糖尿病患者的8个月OS。接收机工作特性(ROC)曲线分析,决策曲线分析(DCA)曲线分析,和校准曲线分别绘制在训练集和验证集上，以证明模型的性能。此外，根据风险评分将患者分为高危组和低危组，和Kaplan-Meier(KM)生存曲线用于说明两组之间的生存差异。
■本研究共纳入322例患者。单变量和多变量Cox回归分析确定了ATCDM患者OS的5个独立预后因素：手术，肿瘤大小,年龄,化疗,和放射治疗。3个月的列线图，6个月,并根据这些因素建立了8个月的OS。训练集AUC值(3个月AUC:0.767,6个月AUC:0.789,8个月AUC:0.795)和验证集AUC值(3个月AUC:0.753,6个月AUC:0.798,8个月AUC:0.806)以及校准曲线展示了模型的优异适用性和准确性。此外,DCA曲线表明该模型具有显著的临床净获益.KM曲线还证实了该模型对患者OS的出色分层能力。
■本研究中开发的列线图准确预测ATCDM患者的OS。它可以帮助临床医生为这些患者制定适当的治疗策略。
■我们从SEER数据库中提取了2004年至2019年间诊断为DM的ATC患者的数据，以7:3的比例将它们随机分为训练集和验证集。对训练集依次进行单变量和多变量Cox回归分析，以确定总生存期（OS）的独立预后因素，并构建3个月的列线图。6个月,根据所有确定的独立预后因素，ATC糖尿病患者的8个月OS。接收机工作特性(ROC)曲线分析,决策曲线分析(DCA)曲线分析,和校准曲线分别绘制在训练集和验证集上，以证明模型的性能。此外，根据风险评分将患者分为高危组和低危组，和Kaplan-Meier(KM)生存曲线用于说明两组之间的生存差异。
■本研究共纳入322例患者。单变量和多变量Cox回归分析确定了ATCDM患者OS的5个独立预后因素：手术，肿瘤大小,年龄,化疗,和放射治疗。3个月的列线图，6个月,并根据这些因素建立了8个月的OS。训练集AUC值(3个月AUC:0.767,6个月AUC:0.789,8个月AUC:0.795)和验证集AUC值(3个月AUC:0.753,6个月AUC:0.798,8个月AUC:0.806)以及校准曲线展示了模型的优异适用性和准确性。此外,DCA曲线表明该模型具有显著的临床净获益.KM曲线还证实了该模型对患者OS的出色分层能力。
■本研究中开发的列线图准确预测ATCDM患者的OS。它可以帮助临床医生为这些患者制定适当的治疗策略。
