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Published on: October 13, 2023
Multivariate Analysis of Pleural Invasion of Peripheral Non-Small Cell Lung Cancer-Based Computed Tomography Features
Li-Ping Qi1, Xiao-Ting Li, Yue Yang
1From the Departments of *Radiology, and †Thoracic Oncology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China.
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.
