Multivariate Analysis of Pleural Invasion of Peripheral Non-Small Cell Lung Cancer-Based Computed Tomography Features.

OBJECTIVE

The aim of this study was to comprehensively analyze computed tomography features to improve the diagnostic accuracy of visceral pleural invasion of peripheral non-small cell lung cancer.

METHODS

The computed tomography features of 205 non-small cell lung cancer patients were retrospectively studied. The lesion's relation to the pleura was classified into 5 grades. A multivariate logistic regression analysis was conducted to identify independent factors predicting pleural invasion.

RESULTS

The multivariate logistic regression analysis showed that sex (odds ratio [OR], 1.822; P = 0.080), pleural indentation (OR, 4.111; P < 0.001), tumor density (OR, 2.735; P = 0.008), and distance between the lesion and pleura (OR, 1.981; P = 0.048) were independent predictors of pleural invasion. A patient with a score of 10.6 had an 80% risk of pleural invasion, whereas a score lower than 2 was associated with a lower (20%) risk of pleural invasion.

CONCLUSIONS

Comprehensive consideration of these factors of pleural indentation, sex, tumor density, and distance between the lesion and pleura might improve the diagnosis of pleural invasion.