Liu Ao, Han Anqin, Zhu Hui, Ma Li, Huang Yong, Li Minghuan, Jin Feng, Yang Qiuan, Yu Jinming
School of Medicine, Shandong University, Jinan, China.
Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, China.
Oncotarget. 2017 May 16;8(20):33736-33744. doi: 10.18632/oncotarget.16806.
Many noninvasive methods have been explored to determine the mutation status of the epidermal growth factor receptor (EGFR) gene, which is important for individualized treatment of non-small cell lung cancer (NSCLC). We evaluated whether metabolic tumor volume (MTV), a parameter measured by [18F] fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) might help predict EGFR mutation status in NSCLC. Overall, 87 patients who underwent EGFR genotyping and pretreatment PET/CT between January 2013 and September 2016 were reviewed. Clinicopathologic characteristics and metabolic parameters including MTV were evaluated. Univariate and multivariate analyses were used to assess the independent variables that predict mutation status to create prediction models. Forty-one patients (41/87) were identified as having EGFR mutations. The multivariate analysis showed that patients with lower MTV (MTV≤11.0 cm3, p=0.001) who were non-smokers (p=0.037) and had a peripheral tumor location (p=0.033) were more likely to have EGFR mutations. Prediction models using these criteria for EGFR mutation yielded a high AUC (0.805, 95% CI 0.712-0.899), which suggests that the analysis had good discrimination. In conclusion, NSCLC patients with EGFR mutations showed significantly lower MTV than patients with wild-type EGFR. Prediction models based on MTV and clinicopathologic characteristics could provide more information for the identification of EGFR mutations.
人们已经探索了许多非侵入性方法来确定表皮生长因子受体(EGFR)基因的突变状态,这对于非小细胞肺癌(NSCLC)的个体化治疗很重要。我们评估了代谢肿瘤体积(MTV),这是一种通过[18F]氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描(PET/CT)测量的参数,是否有助于预测NSCLC中的EGFR突变状态。总体而言,我们回顾了2013年1月至2016年9月期间接受EGFR基因分型和治疗前PET/CT的87例患者。评估了包括MTV在内的临床病理特征和代谢参数。采用单因素和多因素分析来评估预测突变状态的独立变量,以创建预测模型。41例患者(41/87)被确定为具有EGFR突变。多因素分析显示,MTV较低(MTV≤11.0 cm3,p = 0.001)、不吸烟(p = 0.037)且肿瘤位于外周(p = 0.033)的患者更有可能发生EGFR突变。使用这些EGFR突变标准的预测模型产生了较高的曲线下面积(AUC,0.805,95%CI 0.712 - 0.899),这表明该分析具有良好的区分度。总之,与野生型EGFR患者相比,具有EGFR突变的NSCLC患者的MTV显著更低。基于MTV和临床病理特征的预测模型可为EGFR突变的识别提供更多信息。