摘要
目的构建脊索瘤患者的预测模型并进行验证。方法从SEER数据库(2004~2015年)中鉴定和收集597例脊索瘤患者。Nomogram是基于建模组420例拥有完整数据的患者建立的。C指数(C-index)和校正曲线确定Nomogram的预测精度和判别能力。结果建立了基于年龄、种族、原发部位及数量、肿瘤分期(TNM)、手术方式、是否放疗、肿瘤转移和肿瘤大小等预后因素的预测模型,C指数为0.778。确定生存概率的校准曲线表明,Nomogram预测结果与实际观测结果吻合较好。年龄>60岁(P<0.001,HR 5.723,95%CI 1.988~16.474)、M1(P<0.001,HR 4.121,95%CI 1.834~9.257)、手术方式(全切除,P<0.01,HR0.416,95%CI 0.236~0.732;根治性扩大切除,P<0.0001,HR 0.251,95%CI 0.143~0.442)是独立预后因素。结论 Nomogram为脊柱脊索瘤患者提供了更准确的预后预测。本研究结果显示,年龄>60岁肿瘤分期M1和不进行手术是显著缩短脊索瘤患者生存时间的独立危险因素。
Objective To construct and validate a prediction model of chordoma.Methods A total of 597 patients with chordoma were identified and collected from the SEER database (2004-2015).Nomogram is based on 420 patients with complete data.Prediction accuracy and discriminability of nomogram were determined by C-index and correction curve.Results Prognostic models based on age,race,primary site and number,tumor stage (TNM),surgical method,radiotherapy,tumor metastasis and tumor size were established with a C-index of 0.778.The calibration curve determining the probability of survival showed that nomogram’s predictions were in good agreement with the actual observations.Age > 60 year (P < 0.001,HR 5.723,95% CI 1.988-16.474),M1 (P < 0.001,HR 4.121,95% CI 1.834-9.257),surgical method (total excision,P < 0.01,HR 0.416,95% CI 0.236-0.732),and radical extended resection (P < 0.0001,HR 0.251,95% CI 0.143-0.442) were independent prognostic factors.Conclusions Nomogram provides a more accurate prognosis for patients with spinal chordoma.Age > 60,distant metastasis to M1 stage and absence of surgery are major independent risk factors shortening the survival time of chordoma patients.
作者
李文乐
胡朝晖
王永辉
高森
刘鹤
覃川
LI Wen-le;HU Zhao-hui;WANG Yong-hui;GAO Sen;LIU He;Qin Chuan(Graduate School of Guangxi University of Traditional Chinese Medicine,Nanning,Guangxi,530000,China)
出处
《中国骨与关节杂志》
CAS
2021年第2期85-92,共8页
Chinese Journal of Bone and Joint
基金
国家自然科学基金(81260274)
柳州市科学与技术研发计划(2014J030405)。
关键词
脊索瘤
模型
统计学
列线图
预后
Chordoma
Models,statistical
Nomogram
Prognosis