PDF(Pubmed)
Abstract：
UNASSIGNED: This study was conducted to develop a comprehensive nomogram for individuals with choroidal melanoma (CM) to determine their cancer-specific survival (CSS).
UNASSIGNED: Data of individuals with CM, diagnosed between 2004 and 2015, were accessed at the Surveillance, Epidemiology, and End Results (SEER) database. The selected individuals were randomly categorized into a training and validation cohort. Multivariate Cox regression analysis was applied to screen the relevant variables. Followed by the development of a nomogram based on independent variables. Ultimately, the net reclassification index (NRI), concordance index (C-index), calibration charts, integrated discrimination improvement (IDI), receiver operating characteristic curves (ROC), area under the curve (AUC), and decision-curve analysis (DCA), were utilized to evaluate the discrimination, accuracy, and effectiveness of the model.
UNASSIGNED: This study enrolled 3,782 patients. Seven independent factors linked to prognosis were screened via multivariate Cox regression analysis, encompassing age at diagnosis; race; AJCC (American Joint Committee on Cancer) stage; histologic type; and therapy method of radiotherapy, surgery, and chemotherapy. The respective C-indexes of the training and validation cohorts were 0.709 and 0.726, indicative of the excellent accuracy of the nomogram. Furthermore, the AUCs of the training and validation cohorts across 3, 5, and 8 years were 0.767, 0.744, and 0.722 as well as 0.772, 0.770, and 0.753, respectively. Evident of the superiority of the established nomogram over the AJCC staging, both the NRI and IDI values exhibited improvement. The favorable clinical impact and good performance of the nomogram were evident via decision curve analyses (DCAs) and calibration plots, respectively.
UNASSIGNED: This research dealt with establishing and validating a nomogram as a prognostic tool for assessing the prognosis of adult patients with CM utilizing the SEER database. A comprehensive assessment of the nomogram via diverse variables demonstrated its accuracy in predicting the CSS probabilities of CM patients across 3, 5, and 8 years in clinical settings. Notably, its performance surpassed that of the AJCC staging system.
UNASSIGNED: Data of individuals with CM, diagnosed between 2004 and 2015, were accessed at the Surveillance, Epidemiology, and End Results (SEER) database. The selected individuals were randomly categorized into a training and validation cohort. Multivariate Cox regression analysis was applied to screen the relevant variables. Followed by the development of a nomogram based on independent variables. Ultimately, the net reclassification index (NRI), concordance index (C-index), calibration charts, integrated discrimination improvement (IDI), receiver operating characteristic curves (ROC), area under the curve (AUC), and decision-curve analysis (DCA), were utilized to evaluate the discrimination, accuracy, and effectiveness of the model.
UNASSIGNED: This study enrolled 3,782 patients. Seven independent factors linked to prognosis were screened via multivariate Cox regression analysis, encompassing age at diagnosis; race; AJCC (American Joint Committee on Cancer) stage; histologic type; and therapy method of radiotherapy, surgery, and chemotherapy. The respective C-indexes of the training and validation cohorts were 0.709 and 0.726, indicative of the excellent accuracy of the nomogram. Furthermore, the AUCs of the training and validation cohorts across 3, 5, and 8 years were 0.767, 0.744, and 0.722 as well as 0.772, 0.770, and 0.753, respectively. Evident of the superiority of the established nomogram over the AJCC staging, both the NRI and IDI values exhibited improvement. The favorable clinical impact and good performance of the nomogram were evident via decision curve analyses (DCAs) and calibration plots, respectively.
UNASSIGNED: This research dealt with establishing and validating a nomogram as a prognostic tool for assessing the prognosis of adult patients with CM utilizing the SEER database. A comprehensive assessment of the nomogram via diverse variables demonstrated its accuracy in predicting the CSS probabilities of CM patients across 3, 5, and 8 years in clinical settings. Notably, its performance surpassed that of the AJCC staging system.
摘要：
■进行该研究以开发脉络膜黑色素瘤（CM）个体的综合列线图，以确定其癌症特异性存活率（CSS）。
■具有CM的个人数据，在2004年至2015年之间被诊断出，在监测中被访问，流行病学,和结束结果(SEER)数据库。将所选择的个体随机分类到训练和验证队列中。采用多因素Cox回归分析筛选相关变量。其次是基于自变量的列线图的发展。最终,净重新分类指数(NRI)，一致性指数(C指数)，校准图表,综合歧视改进(IDI)，接收机工作特性曲线(ROC)，曲线下面积（AUC），和决策曲线分析(DCA)，被用来评估歧视，准确度,和模型的有效性。
■这项研究招募了3,782名患者。通过多因素Cox回归分析筛选与预后相关的7个独立因素。包括诊断年龄;种族;AJCC（美国癌症联合委员会）阶段;组织学类型;和放射治疗方法，手术,和化疗。训练和验证队列各自的C指数分别为0.709和0.726，表明列线图具有出色的准确性。此外，3年,5年和8年的训练和验证队列的AUC分别为0.767,0.744和0.722,以及0.772,0.770和0.753.证明已建立的列线图优于AJCC分期，NRI和IDI值均表现出改善。通过决策曲线分析（DCA）和校准图，列线图具有良好的临床效果和良好的性能。分别。
■本研究涉及利用SEER数据库建立和验证作为评估成年CM患者预后的预后工具的列线图。通过各种变量对列线图的综合评估证明了其在临床环境中预测3、5和8年CM患者的CSS概率的准确性。值得注意的是,它的性能超过了AJCC分期系统。
■具有CM的个人数据，在2004年至2015年之间被诊断出，在监测中被访问，流行病学,和结束结果(SEER)数据库。将所选择的个体随机分类到训练和验证队列中。采用多因素Cox回归分析筛选相关变量。其次是基于自变量的列线图的发展。最终,净重新分类指数(NRI)，一致性指数(C指数)，校准图表,综合歧视改进(IDI)，接收机工作特性曲线(ROC)，曲线下面积（AUC），和决策曲线分析(DCA)，被用来评估歧视，准确度,和模型的有效性。
■这项研究招募了3,782名患者。通过多因素Cox回归分析筛选与预后相关的7个独立因素。包括诊断年龄;种族;AJCC（美国癌症联合委员会）阶段;组织学类型;和放射治疗方法，手术,和化疗。训练和验证队列各自的C指数分别为0.709和0.726，表明列线图具有出色的准确性。此外，3年,5年和8年的训练和验证队列的AUC分别为0.767,0.744和0.722,以及0.772,0.770和0.753.证明已建立的列线图优于AJCC分期，NRI和IDI值均表现出改善。通过决策曲线分析（DCA）和校准图，列线图具有良好的临床效果和良好的性能。分别。
■本研究涉及利用SEER数据库建立和验证作为评估成年CM患者预后的预后工具的列线图。通过各种变量对列线图的综合评估证明了其在临床环境中预测3、5和8年CM患者的CSS概率的准确性。值得注意的是,它的性能超过了AJCC分期系统。
