A new predictive scoring system based on clinical data and computed tomography features for diagnosing EGFR-mutated lung adenocarcinoma

作者：Cao, Y.^*; Xu, H.^*

来源：Current Oncology, 2018, 25(2): E132-E138.

DOI：10.3747/co.25.3805

• Computed tomography
• epidermal growth factor receptor
• lung cancer
• 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 x 16 + smoking history x 15 + GGO x 12 + pericardial effusion x 10 + emphysema x 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.

• 出版日期2018-4
• 单位武汉大学