Correlations Study Between F-FDG PET/CT Metabolic Parameters Predicting Epidermal Growth Factor Receptor Mutation Status and Prognosis in Lung Adenocarcinoma.

This study assessed the ability of metabolic parameters from Fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) and clinicopathological data to predict epidermal growth factor receptor (EGFR) expression/mutation status in patients with lung adenocarcinoma and to develop a prognostic model based on differences in expression status, to enable individualized targeted molecular therapy. Metabolic parameters and clinicopathological data from 200 patients diagnosed with lung adenocarcinoma between July 2009 and November 2016, who underwent F-FDG PET/CT and mutation testing, were retrospectively evaluated. Multivariate logistic regression was applied to significant variables to establish a prediction model for mutation status. Overall survival for both mutant and wild-type was analyzed to establish a multifactor Cox regression model. Of the 200 patients, 115 (58%) exhibited mutations and 85 (42%) were wild-type. Among selected metabolic parameters, metabolic tumor volume (MTV) demonstrated a significant difference between wild-type and mutant mutation status, with an area under the receiver operating characteristic curve (AUC) of 0.60, which increased to 0.70 after clinical data (smoking status) were combined. Survival analysis of wild-type and mutant yielded mean survival times of 34.451 (95% CI 28.654-40.249) and 53.714 (95% CI 44.331-63.098) months, respectively. Multivariate Cox regression revealed that mutation type, tumor stage, and thyroid transcription factor-1 (TTF-1) expression status were the main factors influencing patient prognosis. The hazard ratio for mutant was 0.511 (95% CI 0.303-0.862) times that of wild-type, and the risk of death was lower for mutant than for wild-type. The risk of death was lower in TTF-1-positive than in TTF-1-negative patients. F-FDG PET/CT metabolic parameters combined with clinicopathological data demonstrated moderate diagnostic efficacy in predicting mutation status and were associated with prognosis in mutant and wild-type non-small-cell lung cancer (NSCLC), thus providing a reference for individualized targeted molecular therapy.