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

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Abstract

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.

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