目的:本研究旨在确定非小细胞肺癌患者的特定临床和计算机断层扫描(CT)模式的存在是否与表皮生长因子受体(EGFR)突变有关。
方法:在2002年1月至2021年7月之间在6个数据库中进行了系统的文献综述和荟萃分析。使用比值比(OR)测量并合并临床和CT模式以检测EGFR突变之间的关系。这些结果用于建立几个数学模型来预测EGFR突变。
结果:34项回顾性诊断准确性研究符合纳入和排除标准。结果表明,毛玻璃不透明度(GGO)的OR为1.86(95CI1.34-2.57),空气支气管造影OR1.60(95CI1.38-1.85),血管会聚OR1.39(95CI1.12-1.74),胸膜回缩OR1.99(95CI1.72-2.31),刺突或1.42(95CI1.19-1.70),空化或0.70(95CI0.57-0.86),早期疾病阶段OR1.58(95CI1.14-2.18),非吸烟者状态或2.79(95CI2.34-3.31),女性OR2.33(95CI1.97-2.75)。建立了数学模型,包括评估的所有临床和CT模式,曲线下面积(AUC)为0.81。
结论:GGO,空气支气管图,血管会聚,胸膜回缩,针状边缘,疾病早期阶段,女性性别,和非吸烟状态是EGFR突变的重要危险因素。同时,空化是EGFR突变的保护因素。建立的数学模型可以很好地预测肺腺癌患者的EGFR突变。
OBJECTIVE: This study aims to determine if the presence of specific clinical and computed tomography (CT) patterns are associated with epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer.
METHODS: A systematic literature review and meta-analysis was carried out in 6 databases between January 2002 and July 2021. The relationship between clinical and CT patterns to detect EGFR mutation was measured and pooled using odds ratios (OR). These results were used to build several mathematical models to predict EGFR mutation.
RESULTS: 34 retrospective diagnostic accuracy studies met the inclusion and exclusion criteria. The results showed that ground-glass opacities (GGO) have an OR of 1.86 (95%CI 1.34 -2.57), air bronchogram OR 1.60 (95%CI 1.38 - 1.85), vascular convergence OR 1.39 (95%CI 1.12 - 1.74), pleural retraction OR 1.99 (95%CI 1.72 - 2.31), spiculation OR 1.42 (95%CI 1.19 - 1.70), cavitation OR 0.70 (95%CI 0.57 - 0.86), early disease stage OR 1.58 (95%CI 1.14 - 2.18), non-smoker status OR 2.79 (95%CI 2.34 - 3.31), female gender OR 2.33 (95%CI 1.97 - 2.75). A mathematical model was built, including all clinical and CT patterns assessed, showing an area under the curve (AUC) of 0.81.
CONCLUSIONS: GGO, air bronchogram, vascular convergence, pleural retraction, spiculated margins, early disease stage, female gender, and non-smoking status are significant risk factors for EGFR mutation. At the same time, cavitation is a protective factor for EGFR mutation. The mathematical model built acts as a good predictor for EGFR mutation in patients with lung adenocarcinoma.