肺腺癌临床及CT特征在预测EGFR基因突变中的价值

摘要/Abstract

摘要： 目的分析肺腺癌临床及CT特征在预测表皮生长因子受体（EGFR）基因突变中的价值。方法利用便利抽样的方法,选择2017年1月—2022年12月在北京大学肿瘤医院内蒙古医院接受治疗的791例肺腺癌患者的数据实施回顾性分析。完成肺腺癌患者EGFR基因突变的预测影响因素的单因素、多因素分析,进行肺腺癌患者EGFR基因突变的预测模型构建和预测效能分析。结果纳入调查的791例患者之中,有420例患者的EGFR基因突变为阳性（53.10%）。单因素分析结果显示,EGFR基因突变阳性和阴性患者的吸烟、磨玻璃密度影、充气支气管征、血管集束征、胸膜牵拉征、双肺多发转移的数据差异有统计学意义（P<0.05）。Logistic多因素分析结果显示,吸烟、磨玻璃密度影、充气支气管征、血管集束征、胸膜牵拉征、双肺多发转移是肺腺癌患者EGFR基因突变的独立影响因素（P<0.05）。依据多因素分析所筛选出来的变量构建列线图风险模型,C-index为0.786。利用Logistic回归模型P值预测概率,采用受试者操作特征（ROC）曲线对肺腺癌患者EGFR基因突变的列线图模型预测效能实施分析,约登指数为61.98%。结论不吸烟、存在磨玻璃密度影、充气支气管征、胸膜牵拉征、血管集束征及双肺多发转移可作为肺腺癌患者出现EGFR基因突变的独立预测因子。通过以上因子构建的数学模型可以起到良好的预测效果。

关键词: 肺腺癌, CT, 基因突变, 吸烟, 血管集束征, 胸膜牵拉征

Abstract: Objective To analyze the clinical and CT features of lung adenocarcinoma in predicting EGFR gene mutation. Methods The data of 791 patients with lung adenocarcinoma who were treated in Inner Mongolia Hospital of Peking University Cancer Hospital from January 2017 to December 2022 were retrospectively analyzed by convenience sampling. Univariate and multivariate analysis of predictive factors of EGFR gene mutation in patients with lung adenocarcinoma wasfinished. Construction of predictive model of EGFR gene mutation in lung adenocarcinoma patients and analysis of predictive efficacy was done. Results Of the 791 patients included in the study, 420 (53.10%) were positive for EGFR mutations. The results of single factor analysis showed that there were statistically significant differences in smoking, ground glass density shadow, air bronchial sign, vascular cluster sign, pleural pull sign and multiple metastasis in both lungs between EGFR mutation positive and negative patients (P<0.05). logistic multivariate analysis showed that smoking, ground glass density shadow, air bronchial sign, vascular bunching sign, pleural pull sign and double lung multiple metastasis were independent influencing factors of EGFR gene mutation in lung adenocarcinoma patients (P<0.05). The nomogram risk model was constructed based on the variables screened by multi-factor analysis, and the C-index was 0.786. The P value of logistic regression model was used to predict the probability, and ROC curve was used to analyze the prediction efficiency of EGFR gene mutation in lung adenocarcinoma patients. The Jorden index was 61.98%. Conclusion Non-smoking, presence of ground glass density shadow, air bronchial sign, pleural pull sign, vascular bunching sign and multiple metastasis in both lungs can be independent predictors of EGFR mutation in lung adenocarcinoma patients. The mathematical model constructed by the above factors can play a good forecasting effect.

Key words: lung adenocarcinoma, computed tomography, gene mutation, smoking, vascular cluster sign, pleural stretch sign

中图分类号:

R734.2

霍凤芝, 刘毅勇. 肺腺癌临床及CT特征在预测EGFR基因突变中的价值[J]. 中华养生保健, 2024, 42(20): 160-165.

HUO Feng-zhi, LIU Yi-yong. Clinical and CT Features of Lung Adenocarcinoma in Predicting EGFR Mutations[J]. ZHONGHUA YANGSHENG BAOJIAN, 2024, 42(20): 160-165.

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