A New Predictive Scoring System Based on Clinical Data and Computed Tomography Features for Diagnosing EGFR-mutated Lung Adenocarcinoma

Background: We aimed to develop a new EGFR mutation–predictive scoring system to use in screening for EGFR-mutated lung adenocarcinomas (LACS). Methods: The study enrolled 279 patients with LAC, including 121 patients with EGFR wild-type tumours and 158 with EGFR-mutated tumours. The Student t-test, chi-square test, or Fisher exact test was applied to discriminate clinical and computed tomography (CT) features between the two groups. Using a principal component analysis (PCA) model, we derived predictive coefficients for the presence of EGFR mutation in LAC. Results: The EGFR mutation–predictive score includes sex, smoking history, homogeneity, ground-glass opacity (GGO) on imaging, and the presence of pericardial effusion. The PCA predictive model took this form: sex × 16 + smoking history × 15 + GGO × 12 + pericardial effusion × 10 + emphysema × 11. Model scores ranged from 79 to 147. The area under the receiver operating characteristic curve was 0.752 [95% confidence interval (ci): 0.697 to 0.801] in the LAC population at the optimal cut-off value of 109, and the sensitivity and specificity were 68.4% (95% CI: 60.5% to 75.5%) and 74.4% (95% CI: 65.6% to 81.9%) respectively. Conclusions: The EGFR mutation risk scoring system based on clinical data and CT features is noninvasive and user-friendly. The model appears to frame a positive predictive value and was able to determine the value of repeating a biopsy if tissue is limited.