子宫肉瘤(US)是一种罕见的恶性子宫肿瘤,具有侵袭性和快速进展。这项研究的目的是构建综合列线图,以预测基于监测的US患者的癌症特异性生存率(CSS)。流行病学,和结束结果(SEER)数据库。
使用SEER数据库中2010年至2015年美国患者的数据进行了一项基于人群的回顾性研究。他们被随机分为训练队列和验证队列,比率为7:3。进行多因素Cox分析以确定独立的预后因素。随后,建立了一个列线图来预测患者的CSS.通过一致性指数(C指数)和曲线下面积(AUC)评估列线图的辨别和校准。最后,净重新分类改进(NRI),综合歧视改进(IDI),校准绘图,和决策曲线分析(DCA)用于评估新预测模型的效益。
共3861例US患者纳入本研究。正如多变量考克斯分析所揭示的,诊断时的年龄,种族,婚姻状况,保险记录,肿瘤大小,病理分级,组织学类型,SEER阶段,AJCC阶段,手术状态,放疗状态,和化疗状态是独立的预后因素。在我们的列线图中,病理分级与CSS相关性最强,其次是诊断年龄和手术状态。与AJCC暂存系统相比,新的列线图在训练和验证队列中显示出更好的预测性区别性,C指数更高(0.796和0.767vs.分别为0.706和0.713)。此外,AUC值,校准绘图,NRI,IDI,DCA也表现出比传统系统更好的性能。
我们的研究验证了美国的第一个综合列线图,这可以为临床实践中的美国患者提供更准确和个性化的生存预测。
Uterine sarcoma (US) is a rare malignant uterine tumor with aggressive behavior and rapid progression. The purpose of this
study was to constructa comprehensive nomogram to predict cancer-specific survival (CSS) of patients with US-based on the Surveillance, Epidemiology, and End Results (SEER) database.
A retrospective population-based
study was conducted using data from patients with US between 2010 and 2015 from the SEER database. They were randomly divided into a training cohort and a validation cohort ata 7-to-3 ratio. Multivariate Cox analysis was performed to identify independent prognostic factors. Subsequently, a nomogram was established to predict patient CSS. The discrimination and calibration of the nomogram were evaluated by the concordance index (C-index) and the area under the curve (AUC). Finally, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.
A total of 3861 patients with US were included in our
study. As revealed in multivariate Cox analysis, age at diagnosis, race, marital status, insurance record, tumor size, pathology grade, histological type, SEER stage, AJCC stage, surgery status, radiotherapy status, and chemotherapy status were found to be independent prognostic factors. In our nomogram, pathology grade had strongest correlation with CSS, followed by age at diagnosis and surgery status. Compared to the AJCC staging system, the new nomogram showed better predictive discrimination with a higher C-index in the training and validation cohorts (0.796 and 0.767 vs. 0.706 and 0.713, respectively). Furthermore, the AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.
Our
study validated the first comprehensive nomogram for US, which could provide more accurate and individualized survival predictions for US patients in clinical practice.