Aiqi Shi1,2,3, Jianling Wang1,2,3, Yuzhu Wang1,2,3, Guorong Guo1,2,3, Chouchou Fan1,2,3, Jiangyan Liu4,5,6. 1. Department of Nuclear Medicine, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No. 82, Lanzhou, 730030, People's Republic of China. 2. Second Clinical School, Lanzhou University, Lanzhou, 730030, People's Republic of China. 3. Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China. 4. Department of Nuclear Medicine, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No. 82, Lanzhou, 730030, People's Republic of China. ery_liujy@lzu.edu.cn. 5. Second Clinical School, Lanzhou University, Lanzhou, 730030, People's Republic of China. ery_liujy@lzu.edu.cn. 6. Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China. ery_liujy@lzu.edu.cn.
Abstract
OBJECTIVES: To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and 18F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve. RESULTS: The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter. CONCLUSION: Among the metabolic and heterogeneity parameters of 18F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features.
OBJECTIVES: To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and 18F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve. RESULTS: The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter. CONCLUSION: Among the metabolic and heterogeneity parameters of 18F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features.