Department of Radiology, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan, 430071, Hubei, China.
Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Int J Clin Oncol. 2019 Jun;24(6):649-659. doi: 10.1007/s10147-019-01403-3. Epub 2019 Mar 5.
To systematically analyze CT and clinical characteristics to find out the risk factors of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC). Then the significant characteristics were used to set up a mathematic model to predict EGFR mutation in NSCLC.
PubMed, Web of Knowledge and EMBASE up to August 17, 2018 were systematically searched for relevant studies that investigated the evidence of association between CT and clinical characteristics and EGFR mutation in NSCLC. After study selection, data extraction, and quality assessment, the pooled odds ratios (ORs) were calculated. Then from May 2017 to August 2018, all NSCLC received EGFR mutation examination and CT examination in our hospital were chosen to test the prediction model by receiver operating characteristic (ROC) curves.
Seventeen original studies met the inclusion criteria. The results showed that the ORs of ground-glass opacity (GGO), air bronchogram, pleural retraction, vascular convergence, smoking history, female gender were, respectively, 1.93 (P = 0.003), 2.09 (P = 0.03), 1.59 (P < 0.01), 1.61 (P = 0.001), 0.28 (P < 0.01), 0.35 (P < 0.01). The result of speculation, cavitation/bubble-like lucency, lesion shape, margin, pathological stage were, respectively, 1.19 (P = 0.32), 0.99 (P = 0.97), 0.82 (P = 0.42), 1.02 (P = 0.90), 0.77 (P = 0.30). 121 NSCLC received EGFR mutation test were included to test the prediction model. The mathematical model based on the results of meta-analysis was: 0.74 × air bronchogram + 0.46 × pleural retraction + 0.48 × vascular convergence - 1.27 × non-smoking history - 1.05 × female. The area under the ROC curve was 0.68.
Based on the current evidence, GGO presence, air bronchogram, pleural retraction, vascular convergence were significant risk factors of EGFR mutation in NSCLC. And the prediction model can help to predict EGFR mutation status.
系统分析 CT 和临床特征，找出非小细胞肺癌（NSCLC）表皮生长因子受体（EGFR）突变的危险因素。然后利用显著特征建立数学模型，预测 NSCLC 中的 EGFR 突变。
截至 2018 年 8 月 17 日，系统检索 PubMed、Web of Knowledge 和 EMBASE 中关于 CT 和临床特征与 NSCLC 中 EGFR 突变相关性的相关研究。经过研究选择、数据提取和质量评估，计算合并优势比（OR）。然后，2017 年 5 月至 2018 年 8 月，选择我院所有接受 EGFR 突变检查和 CT 检查的 NSCLC，通过受试者工作特征（ROC）曲线对预测模型进行测试。
17 项原始研究符合纳入标准。结果表明，磨玻璃影（GGO）、空气支气管征、胸膜牵拉、血管聚集、吸烟史、女性的 OR 分别为 1.93（P=0.003）、2.09（P=0.03）、1.59（P<0.01）、1.61（P=0.001）、0.28（P<0.01）、0.35（P<0.01）。推测的结果，空洞/泡影样透亮、病变形状、边缘、病理分期分别为 1.19（P=0.32）、0.99（P=0.97）、0.82（P=0.42）、1.02（P=0.90）、0.77（P=0.30）。纳入 121 例 NSCLC 进行 EGFR 突变检测，以检验预测模型。基于荟萃分析结果的数学模型为：0.74×空气支气管征+0.46×胸膜牵拉+0.48×血管聚集-1.27×非吸烟史-1.05×女性。ROC 曲线下面积为 0.68。
基于目前的证据，GGO 存在、空气支气管征、胸膜牵拉、血管聚集是非小细胞肺癌 EGFR 突变的显著危险因素。预测模型有助于预测 EGFR 突变状态。
