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Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions.


ABSTRACT: 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.

SUBMITTER: Tomita H 

PROVIDER: S-EPMC7104273 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions.

Tomita Hayato H   Kuno Hirofumi H   Sekiya Kotaro K   Otani Katharina K   Sakai Osamu O   Li Baojun B   Hiyama Takashi T   Nomura Keiichi K   Mimura Hidefumi H   Kobayashi Tatsushi T  

International journal of endocrinology 20200318


<h4>Results</h4>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 (<i>P</i> = 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  ...[more]

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