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

Y. Cao, H. Xu

Abstract


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.


Keywords


Computed tomography; epidermal growth factor receptor; lung cancer; adenocarcinoma

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DOI: http://dx.doi.org/10.3747/co.25.3805






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ISSN: 1198-0052 (Print) ISSN: 1718-7729 (Online)