Department of Radiology, Tri-Service General Hospital and National Defense Medical Center, 325, Section 2, Cheng-Gong Road, Nei-Hu, Taipei 114, Taiwan, Republic of China.
Eur J Nucl Med Mol Imaging. 2014 Oct;41(10):1889-97. doi: 10.1007/s00259-014-2802-y. Epub 2014 May 23.
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.
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.
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.
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.
表皮生长因子受体(EGFR)突变状态的鉴定对肺腺癌患者的治疗优化至关重要。有时无法获得足够的组织进行 EGFR 突变分析,尤其是在晚期患者中。本研究旨在基于正电子发射断层扫描/计算机断层扫描(PET/CT)中的(18)F-氟脱氧葡萄糖(FDG)摄取和影像学特征以及血清癌胚抗原(CEA)水平,预测肺腺癌患者的 EGFR 突变状态。
我们回顾性分析了 132 例接受 EGFR 突变检测、预处理 FDG PET/CT 和血清 CEA 分析的肺腺癌患者。分析了 EGFR 突变与患者特征、原发肿瘤最大标准摄取值(SUVmax)、血清 CEA 水平和 CT 影像学特征之间的关系。进行了受试者工作特征(ROC)曲线分析,以量化这些因素的预测价值。
在 69 例患者(52.2%)中发现了 EGFR 突变。SUVmax≥6(p=0.002)和 CEA 水平≥5(p=0.013)的患者更有可能发生 EGFR 突变。较大肿瘤(≥3cm)(p=0.023)和边缘无分叶肿瘤(p=0.026)的 CT 特征也与 EGFR 突变相关。多变量分析显示,更高的 SUVmax 和 CEA 水平、从不吸烟和无分叶肿瘤边缘是 EGFR 突变的最显著预测因素。这四个标准的联合使用产生了更高的 ROC 曲线下面积(0.82),表明具有良好的区分能力。
FDG 摄取、CEA 水平、吸烟状态和肿瘤边缘的综合评估可能有助于预测肺腺癌患者的 EGFR 突变状态,尤其是在肿瘤样本不足以进行基因分析或无法进行基因检测时。需要进一步的大规模前瞻性研究来验证这些结果。