Computed tomography and clinical features associated with epidermal growth factor receptor mutation status in stage I/II lung adenocarcinoma. Issue 3 (6th April 2017)

• Record Type: Journal Article
• Authors: Zou, Jiawei; Lv, Tangfeng; Zhu, Suhua; Lu, Zhenfeng; Shen, Qin; Xia, Leilei; Wu, Jie; Song, Yong; Liu, Hongbing
• Is Part Of: Thoracic cancer. Volume 8:Issue 3(2017)
• Page Start: 260
• Page End: 270
• Publication Date: 2017-04-06
• Subjects: Computed tomography (CT) -- epidermal growth factor receptor (EGFR) -- lung adenocarcinoma; Chest -- Cancer -- Periodicals; Chest -- Cancer -- Treatment -- Periodicals; Chest -- Surgery -- Periodicals; 616.99494005
• DOI: 10.1111/1759-7714.12436
• Languages: English
• ISSNs: 1759-7706

Abstract

Abstract : 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 Nanjing General Hospital between December 2010 and May 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. Receiver 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 L858R and 19 deletion 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.