18F-FDG PET/CT metabolic parameters combined with tumor markers for prediction of EGFR gene mutations in lung adenocarcinoma
HUANG Shiming1, YANG Yang2, YIN Liang1, YUE Jianlan1, LIN Zhichun1
1. Department of Nuclear Medicine, Characteristic Medical Center of Chinese People's Armed Police Force,Tianjin 300162, China;
2. Department of Nuclear Medicine,Military Hospital of Xinjiang,Urumqi 830000, China
Objective To investigate the predictive value of 18F-FDG PET/CT metabolic parameters combined with tumor markers for EGFR gene mutations in lung adenocarcinoma. Methods Patients who were diagnosed with primary lung cancer by PET/CT and histopathologically confirmed as cases of lung adenocarcinoma between January 2010 and August 2018 were selected. The basic clinical data, PET/CT imaging features, PET/CT metabolic parameters, tumor marker levels and EGFR gene mutation data were collected.These patients were divided into the mutation group and wild type group according to the occurrence of mutation. Based on univariate logistic regression analysis, the related parameters of predictive value for EGFR gene mutations were included in the prediction model.The working curve of ROC was drawn for these subjects, the cut-off value calculated, the AUC value of each model compared, and a model for predicting EGFR gene mutations was established before the prediction efficiency of the model was analyzed. Results A total of 105cases of lung adenocarcinoma were included, including 32 cases of mutant EGFR (30.5%) and 73 cases of wild-type EGFR (69.5%).Multivariate analysis showed that the difference in gender(χ2=5.74,P=0.017), rate of smoking(χ2=4.60,P=0.032), density(χ2=5.77,P=0.016), burr sign(χ2=2.06,P=0.015), intra-pulmonary metastasis(χ2=2.91,P=0.088), SUVmean(t=2.82,P=0.015), and CEA(t=-2.48,P=0.016) was statistically significant between the two groups (P<0.05). Four prediction models were established for EGFR gene mutations, and the corresponding prediction accuracy was 78.1%, 81.0%, 78.1%, and 81.9%, respectively. The four prediction models were of statistical significance (P<0.05). Conclusions The single factors related to EGFR gene mutations include smoking, sex, SUVmean, density, burr sign, intrapulmonary metastasis and CEA. The four prediction models based on these factors will help to analyze the occurrence of EGFR gene mutations in patients with lung cancer and guide the individualized treatment with targeted drugs.
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2020, Vol. 31 
