Objective: To differentiate pre-invasive lesion from invasive pulmonary adenocarcinoma (IPA) appearing as ground-glass nodules (GGNs) using CT features.
Methods: 149 GGNs were enrolled in this study, with 74 pure GGNs (p-GGNs) and 75 mixed GGNs (m-GGNs). Firstly, univariate analysis was used to analyse the difference of CT features between pre-invasive lesion and IPA. Then, multivariate analysis was conducted to identify variables that could independently differentiate pre-invasive lesion from IPA. Receiver operating characteristic curve analysis was performed to evaluate the differentiating value of identified variables.
Results: In the p-GGNs, multivariate analysis showed that the amount of blood vessels was an independent risk factor. Using the amount of blood vessels "≥1" as the diagnostic criterion, we could diagnose IPA with a sensitivity of 100%. Using the amount of blood vessels "=0" as the diagnostic criterion, we could diagnose pre-invasive lesions with a specificity of 100%. In the m-GGNs, multivariate analysis showed that the volume of solid portion (VSolid) and pleural indentation were two independent risk factors. One further model was constructed using these two variables: model = 2.508 × (VSolid + 1.407) × (pleural indentation - 1.016). Using the new model, improved diagnostic ability was achieved compared with using VSolid or pleural indentation alone.
Conclusion: The amount of blood vessels through the p-GGNs would be an important criterion during clinical management, while VSolid and pleural indentation seemed important for m-GGNs. Moreover, the new model could further improve the differentiating value for m-GGNs.
Advances in knowledge: CT features are useful in differentiating pre-invasive lesion from IPA appearing as GGNs.