中国癌症杂志 ›› 2020, Vol. 30 ›› Issue (8): 605-610.doi: 10.19401/j.cnki.1007-3639.2020.08.007

• 论著 • 上一篇    下一篇

EGFR突变肺癌脑转移患者的预后评分系统比较

朱丽华 1,2 ,倪婷婷 1,3 ,范兴文 1 ,吴开良 1   

  1. 1. 复旦大学附属肿瘤医院放疗科,复旦大学上海医学院肿瘤学系,上海 200032 ;
    2. 镇江市第一人民医院放疗科,江苏 镇江 212003 ;
    3. 贵州省人民医院放疗科,贵州 贵阳 550002
  • 出版日期:2020-08-30 发布日期:2020-09-03
  • 通信作者: 范兴文 E-mail:wenxingfan@126.com
  • 基金资助:
    国家自然科学基金(81872551,81903252)。

Comparison of prognostic scoring systems in patients with EGFR-mutant lung cancer and brain metastases

ZHU Lihua 1,2 , NI Tingting 1,3 , FAN Xingwen 1 , WU Kailiang 1   

  1. 1. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; 2. Department of Radiation Oncology, Zhenjiang First People’s Hospital, Zhenjiang 212003, Jiangsu Province, China; 3. Department of Radiation Oncology, Guizhou Provincial People’s Hospital, Guiyang 550002, Guizhou Province, China
  • Published:2020-08-30 Online:2020-09-03
  • Contact: FAN Xingwen E-mail: wenxingfan@126.com

摘要: 背景与目的:对肺癌脑转移进行预后分层有助于制定合理的治疗方案,目前肺癌脑转移的预后评分系统包括递归分区分析(recursive partitioning analysis,RPA)、诊断特异性等级预后评估(diagnosis-specific graded prognostic assessment,DS-GPA)标准、脑转移基本评分(basic score for brain metastases,BSBM)及肺癌分子等级预后评估(lung-molecular graded prognostic assessment,lung-mol GPA)标准。比较评估这4种评分系统对表皮生长因子受体(epidermal growth factor receptor,EGFR)基因突变肺癌脑转移预后的预测作用。方法:回顾性分析2008年3月—2018年3月在复旦大学附属肿瘤医院诊治的EGFR突变肺癌脑转移,并接受过酪氨酸激酶抑制剂和脑部放疗的患者。通过log-rank检验行单因素分析,COX回归模型行多因素分析,探索EGFR突变脑转移的独立预后因素。通过log-rank检验分析4种预后评分系统对总生存期的预测作用,并应用受试者工作特征(receiver operating characteristic,ROC)曲线计算每种评分系统的曲线下面积(area under curve,AUC)。结果:共入组分析126例患者,中位生存时间为39个月。EGFR突变肺癌脑转移的独立预后因素包括KPS评分、颅外转移、初始脑转移、EGFR缺失或突变位点及脑转移数量。PRA、DS-GPA、BSBM和lung-mol GPA这4种评分系统的log-rank检验P值分别为0.032、0.124、0.002和0.003;AUC分别为0.619、0.608、0.615和0.621。结论:除DS-GPA外,PRA、BSBM和lung-mol GPA评分系统对EGFR突变肺癌脑转移的预后均有较好的预测作用,其中lung-mol GPA在目前4种评分系统中是最佳的。

关键词: 肺癌, 脑转移, 预后评分系统, 表皮生长因子受体, 突变

Abstract: Background and purpose: Prognostic stratification of lung cancer brain metastases helps to develop a rational treatment plan. The current prognostic scoring system for lung cancer with brain metastasis includes: recursive partitioning analysis (RPA) classes, diagnosis-specific graded prognostic assessment (DS-GPA) index, basic score for brain metastases (BSBM) and lung-molecular graded prognostic assessment (lung-mol GPA). This study aimed to predict the prognosis of epidermal growth factor receptor (EGFR) gene mutant lung cancer with brain metastases using the four scoring systems. Methods: Patients with EGFR-mutant lung cancer and brain metastases diagnosed at the Fudan University Shanghai Cancer Center from Mar. 2008 to Mar. 2018, and patients who received tyrosine kinase inhibitors and brain radiotherapy were analyzed retrospectively. Independent prognostic factors of EGFR mutation brain metastasis were explored using the univariate analysis of log-rank test and COX regression model. The predictive effects of the 4 prognostic scoring systems on overall survival were analyzed by log-rank test, and the area under the curve (AUC) of each scoring system was calculated using the receiver operating characteristic (ROC) curve. Results: A total of 126 patients were enrolled in the study. The median survival time was 39 months. Independent prognostic factors for EGFR-mutant lung cancer with brain metastases included KPS score, extracranial metastasis, initial brain metastasis, EGFR locus and number of brain metastases. The P values of log-rank test for PRA, DS-GPA, BSBM and lung-mol GPA scoring systems were 0.032, 0.124, 0.002 and 0.003, respectively; the AUC values were 0.619, 0.608, 0.615 and 0.621, respectively. Conclusion: Except DS-GPA, the PRA, BSBM and lung-mol GPA scoring systems have a good predictive effect. Lung-mol GPA is the best among the four scoring systems. How to optimize the prognostic prediction model of EGFR-mutant lung cancer with brain metastasis is worth exploring further.

Key words: Lung cancer, Brain metastasis, Prognostic scoring system, Epidermal growth factor receptor, Mutation