The value of predicting the invasiveness and degree of infiltration of pulmonary ground-glass nodules based on computed tomography features and enhanced quantitative analysis.

BACKGROUND

The incidence and mortality rate of lung cancer are the highest in the world among all malignant tumors. Accurate assessment of ground-glass nodules (GGNs) is crucial in reducing lung cancer mortality. This study aimed to explore the value of computed tomography (CT) features and quantitative parameters in predicting the invasiveness and degree of infiltration of GGNs.

METHODS

Lesions were classified into three groups based on pathological types: the precursor glandular lesion (PGL) group, including atypical adenomatoid hyperplasia and adenocarcinoma ; the minimally invasive adenocarcinoma group; and the invasive adenocarcinoma group. Quantitative and qualitative data of the nodules were compared, and receiver operating characteristic (ROC) curve analysis was performed for each quantitative parameter. Binary logistic regression analysis was used to evaluate independent predictors of GGN invasiveness.

RESULTS

There were significant differences in lesion size, morphology, nodule type, bronchial abnormality, internal vascular sign and pleural retraction among the three groups (P<0.05). There were significant differences in all CT quantitative parameters (CT attenuation value in the plain phase, CT attenuation value in the arterial phase, CT attenuation value in the venous phase, arterial phase enhancement difference, venous phase enhancement difference, arterial phase enhancement index and venous phase enhancement index) among the three groups (P<0.001). The ROC curve analysis showed that the CT attenuation value in the plain phase, CT attenuation value in each enhanced phase, enhancement difference and enhancement index had good discriminatory power. Binary logistic regression analysis revealed that nodule type and internal vascular sign were independent risk factors for GGN invasiveness.

CONCLUSIONS

CT features combined with enhanced scanning and quantitative analysis have important value in predicting the invasiveness of GGNs. The type of pulmonary nodule detected on CT (pure GGN or mixed GGN) and the presence of internal vascular signs are independent risk factors for GGN invasiveness.