Abstract
PurposeThe purpose of this study was to create a mathematical model based on the metabolic parameters of PET/CT with clinicopathological characteristics to predict the EGFR mutation status of patients with lung adenocarcinoma.MethodsThis study retrospectively enrolled patients with lung adenocarcinoma who underwent surgical treatment at two centres in China between January 2012 and December 2015. PET/CT metabolic parameters and Classical EGFR mutation status detection by molecular pathology were performed before and after surgery, and we analysed the associations of EGFR mutation status with patient sex, age, smoking history, maximum primary lesion diameter, carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 19 fragment (CYFRA21-1), TNM stage and histopathological subtype of lung adenocarcinoma.ResultsA total of 310 patients were included, comprising 161 with EGFR mutations (51.9%) and 149 with wild-type EGFR (48.1%). EGFR mutations were more common in females, non-smokers, and those with stage IV disease, a low SUVmax, and ≤35 mm nodules, whereas wild-type EGFR was more common in males, smokers, and those with a solid growth pattern. Multivariate analysis suggested that liver SUVratio, smoking history, tumour size, TNM stage, and solid growth pattern can predict EGFR mutation status, and these factors were used to construct a mathematical model.ConclusionThe prediction model constructed in this study based on clinicopathological characteristics and PET/CT parameters might offer a basis by which to predict Classical EGFR status and provide a certain reference value for guiding the use of EGFR-tyrosine kinase inhibitor (EGFR-TKI) treatment in patients with lung adenocarcinoma.
Determinants of Success in EGFR Mutation Status Analysis in EBUS-TBNA Specimens: The Role of PET-CT
