Clinical and CT Features of Lung Adenocarcinoma in Predicting EGFR Mutations

Abstract

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

CLC Number:

R734.2

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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