UNASSIGNED:由于原发性阴道癌(VC)的极度罕见,尚未开发任何模型来预测VC患者的生存概率。我们旨在开发和验证模型来预测VC患者的总体生存率(OS)和癌症特异性生存率(CSS)。
UNASSIGNED:使用2004-2018年监测进行了一项基于人群的多中心回顾性队列研究,流行病学,和美国的最终结果计划数据库。使用Brier评分和Harrell'sC一致性统计量(C-统计量)鉴定最终的多变量Cox模型。决策曲线,校准图,和随时间变化的受试者工作特征曲线(AUC)下的面积用于评估模型预测性能。进行多次填补,然后进行引导。Bootstrap验证涵盖了从模型选择到基线生存和系数计算的整个统计过程。生成预测OS和CSS的列线图。
未经批准:在2,417名符合条件的患者中,1,692和725被随机分配到训练和验证队列。两个队列的中位年龄(四分位数范围)分别为66(56-78)和65(55-76),分别。我们的模型在预测VC患者的生存率方面比美国癌症联合委员会有更大的净收益。在预期事件和观察事件之间表现出很强的辨别能力和极好的一致性。我们的模型的性能指标在三个队列中计算:训练队列,验证队列的完整案例,和估算的验证队列。对于三个队列中的操作系统模型,C统计量分别为0.761、0.752和0.743。校准图的斜率为1.017、1.005和0.959。3年和5年AUC分别为0.795和0.810、0.768和0.771、0.770和0.767。对于三个队列中的CSS模型,C统计量分别为0.775、0.758和0.755。坡度分别为1.021、0.939和0.977。3年和5年AUC分别为0.797和0.793、0.786和0.788、0.757和0.757。
UNASSIGNED:我们率先为VC患者开发和验证了典型的生存预测模型,并生成了相应的列线图,以实现个性化的生存预测,并可以帮助临床医生进行适应风险的随访和治疗。
UNASSIGNED: No models have been developed to predict the survival probability for women with primary vaginal cancer (VC) due to VC\'s extreme rareness. We aimed to develop and validate models to predict the overall survival (OS) and cancer-specific survival (CSS) of VC patients.
UNASSIGNED: A population-based multicenter retrospective cohort
study was carried out using the 2004-2018 Surveillance, Epidemiology, and End Results Program database in the United States. The final multivariate Cox model was identified using the Brier score and Harrell\'s C concordance statistic (C-statistic). The decision curve, calibration plot, and area under the time-dependent receiver operating characteristic curve (AUC) were used to evaluate model prediction performance. Multiple imputation followed by bootstrap was performed. Bootstrap validation covered the entire statistic procedure from model selection to baseline survival and coefficient calculation. Nomograms predicting OS and CSS were generated.
UNASSIGNED: Of the 2,417 eligible patients, 1,692 and 725 were randomly allocated to the training and validation cohorts. The median age (Interquartile range) was 66 (56-78) and 65 (55-76) for the two cohorts, respectively. Our models had larger net benefits in predicting the survival of VC patients than the American Joint Committee on Cancer stage, presenting great discrimination ability and excellent agreement between the expected and observed events. The performance metrics of our models were calculated in three cohorts: the training cohort, complete cases of the validation cohort, and the imputed validation cohort. For the OS model in the three cohorts, the C-statistics were 0.761, 0.752, and 0.743. The slopes of the calibration plots were 1.017, 1.005, and 0.959. The 3- and 5-year AUCs were 0.795 and 0.810, 0.768 and 0.771, and 0.770 and 0.767, respectively. For the CSS model in the three cohorts, the C-statistics were 0.775, 0.758, and 0.755. The slopes were 1.021, 0.939, and 0.977. And the 3- and 5-year AUCs were 0.797 and 0.793, 0.786 and 0.788, and 0.757 and 0.757, respectively.
UNASSIGNED: We were the first to develop and validate exemplary survival prediction models for VC patients and generate corresponding nomograms that allow for individualized survival prediction and could assist clinicians in performing risk-adapted follow-up and treatment.