基于EGFR基因突变状态肺腺癌脑转移预后分级模型的建立
发布时间：2018-12-16 17:16
【摘要】：目的:为更好指导临床决策及预测预后,美国放射肿瘤协作组(RTOG)先后建立多个恶性肿瘤脑转移预后分级指数,包括RPA、BSBM、GPA,但是这些预后分级指数是根据不同原发灶来源恶性脑转移瘤而制定,对具体来源的肿瘤脑转移没有很强针对性。事实上,来源不同的颅脑转移瘤,可能有不同的预后因素,而肿瘤内在分子生物学因素也是颅脑转移瘤的预后因素。相关研究证实,EGFR突变状态与NSCLC密切相关,尤其是肺腺癌。我们拟进一步证实EGFR突变状态与肺腺癌脑转移患者预后的相关性,从而根据EGFR基因突变状态制定肺腺癌脑转移预后分级模型。方法:回顾性分析我院304例经EGFR基因突变检测的肺腺癌脑转移患者:1.筛选脑转移的独立预后因素:Kaplan-Meier法计算患者生存率,各亚组生存率差异比较采用Logrank检验,Cox模型进行多因素预后分析。2.建立预后分级模型:根据多因素分析的结果中的独立预后因素,建立新的预后分级模型。3.其预测能力与其他三种已经公布的预后分级指数进行对比。结果:COX多因素分析显示EGFR基因突变状态(P0.001)与KPS(P0.001)评分为肺腺癌独立的预后因素。将以上两因素纳入新的预后模型中,根据文献描述的方法,利用6个月生存率权重赋值,根据累计得分将其分为低、中、高危组。在本组病例中,Logrank检验显示新的预后分级模型与其他三种已知的预后指数各亚组间均具有统计学差异(P0.001)。新的预后模型的3、6、12月生存率与三种已知的预后分级指数进行对比,均显示出明显优越性。此外,新的预后分级模型针对不同治疗方式的不同组间也具有明显统计学差异(P0.001)。结论:根据EGFR基因突变状态而制定的新的预后分级模型可以用来评估肺腺癌脑转移的预后情况,较其他三种公认的预后分级指数具有明显的优越性,但还需要未来大量的前瞻性研究进一步证实。
[Abstract]:Objective: in order to better guide clinical decision-making and predict prognosis, the United States radiation oncology cohort (RTOG) successively established several malignant tumor brain metastasis prognosis grading index, including RPA,BSBM,GPA,. However, these prognostic grading indices are based on malignant brain metastases from different primary tumors, and have no strong pertinence for specific tumor metastasis. In fact, different brain metastases may have different prognostic factors, and the intrinsic molecular biological factors are also the prognostic factors of craniocerebral metastases. Related studies confirm that EGFR mutation is closely related to NSCLC, especially lung adenocarcinoma. We intend to further confirm the correlation between the EGFR mutation status and the prognosis of patients with brain metastasis of lung adenocarcinoma, so as to establish the prognosis classification model of brain metastasis of lung adenocarcinoma according to the mutation status of EGFR gene. Methods: a retrospective analysis of 304 patients with brain metastases from lung adenocarcinoma detected by EGFR gene mutation: 1. Screening the independent prognostic factors of brain metastasis: Kaplan-Meier method was used to calculate the survival rate of patients, the difference of survival rate among the subgroups was compared by Logrank test and Cox model was used for multivariate prognostic analysis. 2. To establish a prognostic classification model: according to the independent prognostic factors in the results of multivariate analysis, a new prognostic classification model was established. Its predictive power was compared with three other published prognostic grading indices. Results: COX multivariate analysis showed that EGFR gene mutation status (P0. 001) and KPS score (P0. 001) were independent prognostic factors for lung adenocarcinoma. The above two factors were incorporated into the new prognostic model. According to the method described in the literature, the 6-month survival weight was assigned and divided into low, middle and high risk groups according to the cumulative score. In this study, Logrank test showed that the new prognostic grading model was statistically different from the other three known prognostic indices (P0. 001). Compared with the three known prognostic grading indexes, the 3- and 12- year survival rates of the new prognostic model showed significant advantages. In addition, the new prognostic classification model has significant statistical differences among different treatment groups (P0.001). Conclusion: the new prognostic classification model based on the mutation status of EGFR gene can be used to evaluate the prognosis of brain metastasis of lung adenocarcinoma, which is superior to the other three generally accepted prognostic grading indexes. However, a large number of prospective studies in the future need to be further confirmed.
【学位授予单位】：山西医科大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R734.2
本文编号：2382752
[Abstract]:Objective: in order to better guide clinical decision-making and predict prognosis, the United States radiation oncology cohort (RTOG) successively established several malignant tumor brain metastasis prognosis grading index, including RPA,BSBM,GPA,. However, these prognostic grading indices are based on malignant brain metastases from different primary tumors, and have no strong pertinence for specific tumor metastasis. In fact, different brain metastases may have different prognostic factors, and the intrinsic molecular biological factors are also the prognostic factors of craniocerebral metastases. Related studies confirm that EGFR mutation is closely related to NSCLC, especially lung adenocarcinoma. We intend to further confirm the correlation between the EGFR mutation status and the prognosis of patients with brain metastasis of lung adenocarcinoma, so as to establish the prognosis classification model of brain metastasis of lung adenocarcinoma according to the mutation status of EGFR gene. Methods: a retrospective analysis of 304 patients with brain metastases from lung adenocarcinoma detected by EGFR gene mutation: 1. Screening the independent prognostic factors of brain metastasis: Kaplan-Meier method was used to calculate the survival rate of patients, the difference of survival rate among the subgroups was compared by Logrank test and Cox model was used for multivariate prognostic analysis. 2. To establish a prognostic classification model: according to the independent prognostic factors in the results of multivariate analysis, a new prognostic classification model was established. Its predictive power was compared with three other published prognostic grading indices. Results: COX multivariate analysis showed that EGFR gene mutation status (P0. 001) and KPS score (P0. 001) were independent prognostic factors for lung adenocarcinoma. The above two factors were incorporated into the new prognostic model. According to the method described in the literature, the 6-month survival weight was assigned and divided into low, middle and high risk groups according to the cumulative score. In this study, Logrank test showed that the new prognostic grading model was statistically different from the other three known prognostic indices (P0. 001). Compared with the three known prognostic grading indexes, the 3- and 12- year survival rates of the new prognostic model showed significant advantages. In addition, the new prognostic classification model has significant statistical differences among different treatment groups (P0.001). Conclusion: the new prognostic classification model based on the mutation status of EGFR gene can be used to evaluate the prognosis of brain metastasis of lung adenocarcinoma, which is superior to the other three generally accepted prognostic grading indexes. However, a large number of prospective studies in the future need to be further confirmed.
【学位授予单位】：山西医科大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R734.2
【参考文献】
中国期刊全文数据库 前1条
1 曹进;曾川;范卫东;张献全;;2016年ASCO会议肺癌靶向治疗的相关进展[J];中华肺部疾病杂志(电子版);2016年04期
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