非小细胞肺癌术后患者脑转移风险预测列线图的构建

发布时间：2018-05-29 05:49

本文选题：非小细胞肺癌 + 脑转移　；参考：《医学研究生学报》2017年08期

【摘要】：目的脑转移的发生导致非小细胞肺癌(NSCLC)患者的生存时间明显减少,但目前尚无有效的预测工具。文中旨在建立一个有效的、可行的列线图用于预测NSCLC术后患者的脑转移发生风险。方法回顾性分析2010年1月至2014年1月马鞍山市人民医院和江阴市人民医院肿瘤科就诊的接受根治性切除术的NSCLC患者636例。分析术后发生脑转移的高危因素,应用逻辑回归方法 R语言软件建立列线图;计算C-index指标的可信区间以及对模型过拟合的程度进行内部验证,评估其稳定性。结果 636例NSCLC患者术后发生脑转移94例。logistic回归分析结果显示,吸烟史(OR=1.783,95%CI:1.037~3.066)、病理类型(OR=0.453,95%CI:0.275~0.744)、T分期(OR=2.047,95%CI:1.511~2.774)、N分期(OR=1.588,95%CI:1.154~2.184)是脑转移发生的危险因素。列线图的一致系数为0.73(0.71~0.82),平均错误率为0.012,提示列线图具备优越的稳定性。结论所得列线图可以用于个体NSCLC患者术后发生脑转移的风险预测,有助于相应的随访和预防策略针对性的开展,最终使此类患者获益。
[Abstract]:Objective to reduce the survival time of NSCLC patients due to brain metastasis, but there is no effective tool for predicting NSCLC. The purpose of this paper is to establish an effective and feasible line graph to predict the risk of brain metastasis after NSCLC. Methods from January 2010 to January 2014, 636 patients with NSCLC underwent radical resection in the Department of Oncology, Ma'anshan people's Hospital and Jiangyin people's Hospital. The high risk factors of brain metastasis after operation were analyzed, and the logistic regression software R was used to establish the line diagram, to calculate the confidence interval of C-index and to verify the degree of over-fitting of the model, and to evaluate its stability. Results the logistic regression analysis of 94 cases of brain metastases after operation in 636 patients with NSCLC showed that the history of smoking was 1.78395CI1.037 / 3.066, and the pathological type was 0.453C95CI0.275% 0.2750.275% 0.7444T stage OR2.047-95% CI1.5112.7744-N stage OR1.588: 95CI1.1542.1844) was the risk factor of brain metastasis. The concordance coefficient of the line graph is 0.730.71 / 0.82 and the average error rate is 0.012, which indicates that the line graph has superior stability. Conclusion the results obtained can be used to predict the risk of brain metastasis in patients with NSCLC after operation.
【作者单位】：马鞍山市人民医院肿瘤科;江阴市人民医院肿瘤科;马鞍山市人民医院呼吸科;
【分类号】：R734.2

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