Abstract：
BACKGROUND: Anaplastic thyroid cancer (ATC) is a highly aggressive malignancy with dismal prognosis. This study aimed to identify the independent risk factors and construct a readily-to-use nomogram to predict the probability of early death in ATC patients.
METHODS: Patients diagnosed with ATC between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled in this study for model development and internal validation. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for early death of ATC. Nomograms for predicting the probability of all-cause early death (ACED) and cancer-specific early death (CSED) of ATC were subsequently developed. The performance of the nomograms was comprehensively evaluated and validated in an internal cohort.
RESULTS: A total of 696 ATC patients were included in this study, of which 488 patients in the training cohort and 208 patients in the validation cohort. The univariate and multivariate logistic regression analyses identified five independent factors (tumor size, M stage, surgery, radiotherapy and chemotherapy) in the ACED model and six variables in the CSED (gender, tumor size, M stage, surgery, radiotherapy and chemotherapy) model for the establishment of the nomograms. Calibration curves and receiver operating characteristic (ROC) curves showed satisfactory efficacy and consistency both in the training (ACED: AUC values: 0.814 (0.776-0.852); CSED: 0.778 (0.736-0.820)) and validation sets (ACED: 0.762 (0.696-0.827); CSED: 0.745 (0.678-0.812)). In addition, the decision curve analysis (DCA) demonstrated the favorable potential of the two nomograms in clinical application.
CONCLUSIONS: The two nomograms assist clinicians to identify risk factors and predict the early death probability among ATC patients, thus guide individualized treatment to improve the prognosis.
METHODS: Patients diagnosed with ATC between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled in this study for model development and internal validation. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for early death of ATC. Nomograms for predicting the probability of all-cause early death (ACED) and cancer-specific early death (CSED) of ATC were subsequently developed. The performance of the nomograms was comprehensively evaluated and validated in an internal cohort.
RESULTS: A total of 696 ATC patients were included in this study, of which 488 patients in the training cohort and 208 patients in the validation cohort. The univariate and multivariate logistic regression analyses identified five independent factors (tumor size, M stage, surgery, radiotherapy and chemotherapy) in the ACED model and six variables in the CSED (gender, tumor size, M stage, surgery, radiotherapy and chemotherapy) model for the establishment of the nomograms. Calibration curves and receiver operating characteristic (ROC) curves showed satisfactory efficacy and consistency both in the training (ACED: AUC values: 0.814 (0.776-0.852); CSED: 0.778 (0.736-0.820)) and validation sets (ACED: 0.762 (0.696-0.827); CSED: 0.745 (0.678-0.812)). In addition, the decision curve analysis (DCA) demonstrated the favorable potential of the two nomograms in clinical application.
CONCLUSIONS: The two nomograms assist clinicians to identify risk factors and predict the early death probability among ATC patients, thus guide individualized treatment to improve the prognosis.
摘要：
背景：间变性甲状腺癌（ATC）是一种高度侵袭性的恶性肿瘤，预后不良。这项研究旨在确定独立的危险因素，并构建易于使用的列线图来预测ATC患者早期死亡的可能性。
方法：2004年至2015年期间被诊断为ATC的患者，流行病学,和最终结果（SEER）数据库被纳入本研究，用于模型开发和内部验证。进行单因素和多因素logistic回归分析以确定ATC早期死亡的独立危险因素。随后开发了用于预测ATC的全因早期死亡（ACED）和癌症特异性早期死亡（CSED）概率的列线图。在内部队列中全面评估和验证了列线图的性能。
结果：本研究共纳入696例ATC患者，其中488名患者在训练队列中，208名患者在验证队列中。单变量和多变量逻辑回归分析确定了五个独立因素(肿瘤大小，M阶段,手术,ACED模型中的放疗和化疗)和CSED中的六个变量(性别，肿瘤大小,M阶段,手术,放疗和化疗）建立列线图的模型。校准曲线和受试者工作特征（ROC）曲线在训练（ACED：AUC值：0.814（0.776-0.852）；CSED：0.778（0.736-0.820））和验证集（ACED：0.762（0.696-0.827）；CSED：0.745（0.678-0.812））均显示出令人满意的功效和一致性。此外,决策曲线分析(DCA)显示了两种列线图在临床应用中的有利潜力.
结论：两个列线图有助于临床医生识别危险因素并预测ATC患者的早期死亡概率，从而指导个体化治疗,改善预后。
方法：2004年至2015年期间被诊断为ATC的患者，流行病学,和最终结果（SEER）数据库被纳入本研究，用于模型开发和内部验证。进行单因素和多因素logistic回归分析以确定ATC早期死亡的独立危险因素。随后开发了用于预测ATC的全因早期死亡（ACED）和癌症特异性早期死亡（CSED）概率的列线图。在内部队列中全面评估和验证了列线图的性能。
结果：本研究共纳入696例ATC患者，其中488名患者在训练队列中，208名患者在验证队列中。单变量和多变量逻辑回归分析确定了五个独立因素(肿瘤大小，M阶段,手术,ACED模型中的放疗和化疗)和CSED中的六个变量(性别，肿瘤大小,M阶段,手术,放疗和化疗）建立列线图的模型。校准曲线和受试者工作特征（ROC）曲线在训练（ACED：AUC值：0.814（0.776-0.852）；CSED：0.778（0.736-0.820））和验证集（ACED：0.762（0.696-0.827）；CSED：0.745（0.678-0.812））均显示出令人满意的功效和一致性。此外,决策曲线分析(DCA)显示了两种列线图在临床应用中的有利潜力.
结论：两个列线图有助于临床医生识别危险因素并预测ATC患者的早期死亡概率，从而指导个体化治疗,改善预后。
