Open AccessComputed tomography and clinical features associated with epidermal growth factor receptor mutation status in stage I/II l ung adenocarcinoma
Author(s) -
Zou Jiawei,
Lv Tangfeng,
Zhu Suhua,
Lu Zhenfeng,
Shen Qin,
Xia Leilei,
Wu Jie,
Song Yong,
Liu Hongbing
Publication year - 2017
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.12436
Subject(s) - medicine , carcinoembryonic antigen , adenocarcinoma , stage (stratigraphy) , epidermal growth factor receptor , ground glass opacity , logistic regression , receiver operating characteristic , lung , t stage , gastroenterology , oncology , radiology , pathology , cancer , biology , paleontology
Background The relationship between epidermal growth factor receptor ( EGFR) gene mutation status, preoperative computed tomography ( CT), and clinical features in patients with small peripheral lung adenocarcinoma (<3 cm) was investigated. Methods We included 209 patients who underwent surgical resection for stage I or II lung adenocarcinoma at N anjing General Hospital between D ecember 2010 and M ay 2016. 171 cases of patients underwent a pretreatment chest CT. Eleven different CT descriptors were assessed. Multiple logistic regression analyses were performed to identify independent risk factors for the prediction of EGFR mutation. R eceiver operating characteristic analysis was used to evaluate the performance of the logistic regression model. Results EGFR mutation was determined in 126 patients (60.3%) and appeared more frequently in women ( P = 0.025), never‐smokers ( P < 0.001), and patients with a carcinoembryonic antigen level <2.6 ng/ml ( P = 0.045). Papillary predominant adenocarcinomas ( P = 0.014), intermediate/low pathologic grade tumors ( P = 0.003), tumors in the upper lobe ( P = 0.028), and showing ground‐glass opacity ( GGO ) or mixed GGO on CT ( P = 0.039) also more frequently harbored EGFR mutations. GGO on CT , acinar or papillary predominant adenocarcinoma, and non‐smoker were identified in multivariable analyses as significantly independent risk factors. The multiple logistic regression model showed high predictive power for identifying EGFR mutations. The CT features were similar between the L 858 R and 19 d eletion mutations. Conclusions Combined CT and clinical features may be helpful for determining the presence of EGFR mutations in patients with small peripheral lung adenocarcinoma, particularly in patients where mutational profiling is not available or possible.