Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
Int J Clin Oncol. 2019 Nov;24(11):1459-1467. doi: 10.1007/s10147-019-01489-9. Epub 2019 Jun 26.
We aimed to develop and validate a reliable nomogram for predicting the disease-specific survival (DSS) of chondrosarcoma patients.
The Surveillance, Epidemiology, and End Results (SEER) database was queried from 2004 to 2015 to identify cases of histologically confirmed chondrosarcoma. Multivariate Cox regression analysis was performed to identify independent prognostic factors and construct a nomogram for predicting the 3- and 5-year DSS rates. Predictive values were compared between the new model and the American Joint Committee on Cancer (AJCC) staging system using concordance indexes (C-indexes), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).
Multivariate Cox regression identified 1180 patients, who were used to establish a nomogram based on a new model containing the predictive variables of age, socioeconomic status, tumor size, surgery status, chemotherapy status, and AJCC staging. In the nomogram, age at diagnosis is the factor with the highest risk, followed by AJCC stage IV and tumor size > 100 mm. Both the C-index and the calibration plots demonstrated the good performance of the nomogram. Moreover, both NRI and IDI were improved compared to the AJCC staging system, and also DCA demonstrated that the nomogram is clinically useful.
We have developed a reliable nomogram for determining the prognosis and treatment outcomes of chondrosarcoma patients that is superior to the traditional AJCC staging system.
我们旨在开发和验证一种可靠的列线图,以预测软骨肉瘤患者的疾病特异性生存(DSS)。
从 2004 年到 2015 年,我们对监测、流行病学和最终结果(SEER)数据库进行了查询,以确定组织学证实的软骨肉瘤病例。使用多变量 Cox 回归分析确定独立的预后因素,并构建用于预测 3 年和 5 年 DSS 率的列线图。使用一致性指数(C 指数)、校准图、综合判别改善(IDI)、净重新分类改善(NRI)和决策曲线分析(DCA)比较新模型和美国癌症联合委员会(AJCC)分期系统之间的预测值。
多变量 Cox 回归分析确定了 1180 例患者,用于建立一个基于新模型的列线图,该模型包含预测变量包括年龄、社会经济状况、肿瘤大小、手术状态、化疗状态和 AJCC 分期。在列线图中,诊断时的年龄是风险最高的因素,其次是 AJCC 分期 IV 和肿瘤大小>100mm。C 指数和校准图均表明列线图具有良好的性能。此外,与 AJCC 分期系统相比,NRI 和 IDI 均得到改善,DCA 也表明列线图具有临床实用性。
我们已经开发了一种可靠的列线图,用于确定软骨肉瘤患者的预后和治疗结果,优于传统的 AJCC 分期系统。