Epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) are a response to EGFR-tyrosine kinase inhibitor. However, a lack of sufficient tumor tissue has been a limitation for determining EGFR mutation status in clinical practice. The objective of this study was to predict EGFR mutation status in NSCLC patients based on a model including maximum standardized uptake value (SUVmax) and clinical features.We retrospectively reviewed NSCLC patients undergoing EGFR mutation testing and pretreatment positron emission tomography/computed tomography between March 2009 and December 2013. The relationships of EGFR mutations with both SUVmax and patient characteristics were evaluated, and a multivariate logistic regression analysis was performed. The model was assessed by area under the receiver-operating characteristic curve (AUC) and was prospectively validated during January to June 2014.Three hundred and sixteen patients meeting the criteria were enrolled for model construction. The SUVmax values were significantly lower for EGFR mutations (mean, 9.5 ± 5.74) than for EGFR wild-type (mean, 12.7 ± 6.43; P < 0.001). ROC curve analysis showed that the SUVmax cutoff point was 8.1, for which the AUC was 0.65 (95% confidence interval [CI], 0.60-0.72). In addition, multivariate analysis also showed that low SUVmax (?8.1) was a predictor of EGFR mutations, for which the AUC was 0.77, combining nonsmoking history and primary tumor size (?5 cm). Eighty-five patients were enrolled to validate the predictive model, and the overall accuracy, sensitivity, and specificity were 77.6%, 64.6% (95% CI 40.7-82.8), and 82.5% (95% CI 70.9-91.0), respectively.The specific FDG uptake value could be considered to effectively predict EGFR mutation status of NSCLC patients by considering smoking history and primary tumor size when genetic tests are not available.
Adult Aged