PDF(Pubmed)
Abstract：
UNASSIGNED: Thyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients.
UNASSIGNED: We collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model.
UNASSIGNED: A total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805-0.927), for the validation cohort it was 0.944 (95 % CI: 0.903-0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997-1.001). Calibration curves and AUC suggest that the model has good accuracy.
UNASSIGNED: We developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.
UNASSIGNED: We collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model.
UNASSIGNED: A total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805-0.927), for the validation cohort it was 0.944 (95 % CI: 0.903-0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997-1.001). Calibration curves and AUC suggest that the model has good accuracy.
UNASSIGNED: We developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.
摘要：
■甲状腺癌（TC）是内分泌系统中最常见的恶性肿瘤，也是头颈部肿瘤之一。滤泡性甲状腺癌（FTC）在甲状腺癌的病理分类中起着重要作用。本研究旨在开发一种创新的预测工具，一个列线图,预测中年FTC患者的癌症特异性生存率（CSS）。
■我们从监测中收集了患者数据，流行病学,和结束结果(SEER)数据库。2004年至2015年患者的数据被用作训练集，2016年至2018年患者的数据被用作验证集.确定影响患者生存的独立危险因素，进行单因素和多因素Cox回归分析.基于此,我们建立了一个列线图模型,用于预测中年FTC患者的CSS.一致性指数(C指数)，接受者工作特征曲线(ROC)下面积(AUC)，和校准曲线用于评估模型的准确性和置信度。
■本研究共纳入2470名患者，其中2004年至2015年的患者被随机分配到训练队列（N=1437）和验证队列（N=598），2016年至2018年的患者按时间分配到外部验证队列(N=435).单因素和多因素Cox回归分析显示,组织学分级和TNM分期是生存的独立危险因素。训练队列的C指数为0.866（95％CI：0.805-0.927），对于验证队列，它是0.944（95%CI：0.903-0.985），对于外部验证队列，达到0.999（95%CI：0.997-1.001）。校准曲线和AUC表明该模型具有良好的准确性。
■我们开发了一种创新的列线图来预测FTC中年患者的CSS。我们的模型经过严格的内部验证和外部验证过程，基于时间证明了其高水平的准确性和可靠性。该工具可帮助医疗保健专业人员和患者做出明智的临床决策。
■我们从监测中收集了患者数据，流行病学,和结束结果(SEER)数据库。2004年至2015年患者的数据被用作训练集，2016年至2018年患者的数据被用作验证集.确定影响患者生存的独立危险因素，进行单因素和多因素Cox回归分析.基于此,我们建立了一个列线图模型,用于预测中年FTC患者的CSS.一致性指数(C指数)，接受者工作特征曲线(ROC)下面积(AUC)，和校准曲线用于评估模型的准确性和置信度。
■本研究共纳入2470名患者，其中2004年至2015年的患者被随机分配到训练队列（N=1437）和验证队列（N=598），2016年至2018年的患者按时间分配到外部验证队列(N=435).单因素和多因素Cox回归分析显示,组织学分级和TNM分期是生存的独立危险因素。训练队列的C指数为0.866（95％CI：0.805-0.927），对于验证队列，它是0.944（95%CI：0.903-0.985），对于外部验证队列，达到0.999（95%CI：0.997-1.001）。校准曲线和AUC表明该模型具有良好的准确性。
■我们开发了一种创新的列线图来预测FTC中年患者的CSS。我们的模型经过严格的内部验证和外部验证过程，基于时间证明了其高水平的准确性和可靠性。该工具可帮助医疗保健专业人员和患者做出明智的临床决策。
