Abstract
PURPOSE: 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.