Use of Computed Tomography–Based Texture Analysis to Differentiate Benign From Malignant Salivary Gland Lesions

Abstract

Objective Salivary gland lesions show overlapping morphological findings and types of time/intensity curves. This research aimed to evaluate the role of 2-phase multislice spiral computed tomography (MSCT) texture analysis in differentiating between benign and malignant salivary gland lesions. Methods In this prospective study, MSCT was carried out on 90 patients. Each lesion was segmented on axial computed tomography (CT) images manually, and 33 texture features and morphological CT features were assessed. Logistic regression analysis was used to confirm predictors of malignancy (P < 0.05 was considered to be statistically significant), followed by receiver operating characteristics analysis to assess the diagnostic performance. Results Univariate logistic regression analysis revealed that morphological CT features (shape, size, and invasion of adjacent tissues) and 17 CT texture parameters had significant differences between benign and malignant lesions (P < 0.05). Multivariate binary logistic regression demonstrated that shape, invasion of adjacent tissues, entropy, and inverse difference moment were independent factors for malignant tumors. The diagnostic accuracy values of multivariate binary logistic models based on morphological parameters, CT texture features, and a combination of both were 87.8%, 90%, and 93.3%, respectively. Conclusions Two-phase MSCT texture analysis was conducive to differentiating between malignant and benign neoplasms in the salivary gland, especially when combined with morphological CT features.