PURPOSE: The identification of the mutation status of the epidermal growth factor receptor (EGFR) is important for the optimization of treatment in patients with pulmonary adenocarcinoma. The acquisition of adequate tissues for EGFR mutational analysis is sometimes not feasible, especially in advanced-stage patients. The aim of this study was to predict EGFR mutation status in patients with pulmonary adenocarcinoma based on (18)F-fluorodeoxyglucose (FDG) uptake and imaging features in positron emission tomography/computed tomography (PET/CT), as well as on the serum carcinoembryonic antigen (CEA) level. METHODS: We retrospectively reviewed 132 pulmonary adenocarcinoma patients who underwent EGFR mutation testing, pretreatment FDG PET/CT and serum CEA analysis. The associations between EGFR mutations and patient characteristics, maximal standard uptake value (SUVmax) of primary tumors, serum CEA level and CT imaging features were analyzed. Receiver-operating characteristic (ROC) curve analysis was performed to quantify the predictive value of these factors. RESULTS: EGFR mutations were identified in 69 patients (52.2 %). Patients with SUVmax ≥6 (p = 0.002) and CEA level ≥5 (p = 0.013) were more likely to have EGFR mutations. The CT characteristics of larger tumors (≥3 cm) (p = 0.023) and tumors with a nonspiculated margin (p = 0.026) were also associated with EGFR mutations. Multivariate analysis showed that higher SUVmax and CEA level, never smoking and a nonspiculated tumor margin were the most significant predictors of EGFR mutation. The combined use of these four criteria yielded a higher area under the ROC curve (0.82), suggesting a good discrimination. CONCLUSION: The combined evaluation of FDG uptake, CEA level, smoking status and tumor margins may be helpful in predicting EGFR mutation status in patients with pulmonary adenocarcinoma, especially when the tumor sample is inadequate for genetic analysis or genetic testing is not available. Further large-scale prospective studies are needed to validate these results.
PURPOSE: The identification of the mutation status of the epidermal growth factor receptor (EGFR) is important for the optimization of treatment in patients with pulmonary adenocarcinoma. The acquisition of adequate tissues for EGFR mutational analysis is sometimes not feasible, especially in advanced-stage patients. The aim of this study was to predict EGFR mutation status in patients with pulmonary adenocarcinoma based on (18)F-fluorodeoxyglucose (FDG) uptake and imaging features in positron emission tomography/computed tomography (PET/CT), as well as on the serum carcinoembryonic antigen (CEA) level. METHODS: We retrospectively reviewed 132 pulmonary adenocarcinomapatients who underwent EGFR mutation testing, pretreatment FDG PET/CT and serum CEA analysis. The associations between EGFR mutations and patient characteristics, maximal standard uptake value (SUVmax) of primary tumors, serum CEA level and CT imaging features were analyzed. Receiver-operating characteristic (ROC) curve analysis was performed to quantify the predictive value of these factors. RESULTS:EGFR mutations were identified in 69 patients (52.2 %). Patients with SUVmax ≥6 (p = 0.002) and CEA level ≥5 (p = 0.013) were more likely to have EGFR mutations. The CT characteristics of larger tumors (≥3 cm) (p = 0.023) and tumors with a nonspiculated margin (p = 0.026) were also associated with EGFR mutations. Multivariate analysis showed that higher SUVmax and CEA level, never smoking and a nonspiculated tumor margin were the most significant predictors of EGFR mutation. The combined use of these four criteria yielded a higher area under the ROC curve (0.82), suggesting a good discrimination. CONCLUSION: The combined evaluation of FDG uptake, CEA level, smoking status and tumor margins may be helpful in predicting EGFR mutation status in patients with pulmonary adenocarcinoma, especially when the tumor sample is inadequate for genetic analysis or genetic testing is not available. Further large-scale prospective studies are needed to validate these results.
Authors: Shengri Liao; Bill C Penney; Kristen Wroblewski; Hao Zhang; Cassie A Simon; Rony Kampalath; Ming-Chi Shih; Naoko Shimada; Sheng Chen; Ravi Salgia; Daniel E Appelbaum; Kenji Suzuki; Chin-Tu Chen; Yonglin Pu Journal: Eur J Nucl Med Mol Imaging Date: 2011-09-23 Impact factor: 9.236
Authors: Rafael Rosell; Teresa Moran; Cristina Queralt; Rut Porta; Felipe Cardenal; Carlos Camps; Margarita Majem; Guillermo Lopez-Vivanco; Dolores Isla; Mariano Provencio; Amelia Insa; Bartomeu Massuti; Jose Luis Gonzalez-Larriba; Luis Paz-Ares; Isabel Bover; Rosario Garcia-Campelo; Miguel Angel Moreno; Silvia Catot; Christian Rolfo; Noemi Reguart; Ramon Palmero; José Miguel Sánchez; Roman Bastus; Clara Mayo; Jordi Bertran-Alamillo; Miguel Angel Molina; Jose Javier Sanchez; Miquel Taron Journal: N Engl J Med Date: 2009-08-19 Impact factor: 91.245
Authors: Jian Guan; Nan J Xiao; Min Chen; Wen L Zhou; Yao W Zhang; Shuang Wang; Yong M Dai; Lu Li; Yue Zhang; Qin Y Li; Xiang Z Li; Mi Yang; Hu B Wu; Long H Chen; Lai Y Liu Journal: Medicine (Baltimore) Date: 2016-07 Impact factor: 1.889