Background and Objectives. Thyroid nodules are increasingly being detected during cross-sectional imaging of the neck and chest. The purpose of this study is to investigate the efficacy of dual-energy computed tomography (DECT) using iodine concentration measurement and multiparametric texture analysis of monochromatic images for differentiating between benign and malignant thyroid nodules. Materials and Methods. This retrospective study included 34 consecutive patients who presented with thyroid nodules and underwent noncontrast DECT between 2015 and 2016. Manual segmentation of each thyroid nodule by monochromatic imaging (40, 60, and 80 keV) was performed, and an in-house developed MATLAB-based texture analysis program was used to extract 41 textures. Iodine material decomposition and CT attenuation slopes were also measured. Histopathologic findings of ultrasound-guided biopsies over a follow-up period of at least one year were used as reference standards. Basic descriptive statistics and areas under receiver operating characteristic curves (AUCs) were evaluated. Results. The 34 nodules comprised 14 benign nodules and 20 malignant nodules. Iodine content and Hounsfield unit curve slopes did not differ significantly between benign and malignant thyroid nodules (P=0.480–0.670). However, significant differences in the texture features of monochromatic images were observed between benign and malignant nodules: histogram mean and median, co-occurrence matrix contrast, gray-level gradient matrix (GLGM) skewness, and mean gradients and variance of gradients for GLGM at 80 keV (P=0.014–0.044). The highest AUC was 0.77, for the histogram mean and median of images acquired at 80 keV. Conclusions. Texture features extracted from monochromatic images using DECT, specifically acquired at high keV, may be a promising diagnostic approach for thyroid nodules. A further large study for incidental thyroid nodules using DECT texture analysis is required to validate our results.
Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions
