Usefulness of Real-Time Quantitative Microvascular Ultrasonography for Differentiation of Graves’ Disease from Destructive Thyroiditis in Thyrotoxic Patients
Endocrinology and Metabolism
; : 323-332, 2022.
Article
em En
| WPRIM
| ID: wpr-924942
Biblioteca responsável:
WPRO
ABSTRACT
Background@#Microvascular ultrasonography (MVUS) is a third-generation Doppler technique that was developed to increase sensitivity compared to conventional Doppler. The purpose of this study was to compare MVUS with conventional color Doppler (CD) and power Doppler (PD) imaging to distinguish Graves’ disease (GD) from destructive thyroiditis (DT). @*Methods@#This prospective study included 101 subjects (46 GDs, 47 DTs, and eight normal controls) from October 2020 to November 2021. All ultrasonography examinations were performed using microvascular flow technology (MV-Flow). The CD, PD, and MVUS images were semi-quantitatively graded according to blood flow patterns. On the MVUS images, vascularity indices (VIs), which were the ratio (%) of color pixels in the total grayscale pixels in a defined region of interest, were obtained automatically. Receiver operating characteristic curve analysis was performed to verify the diagnostic performance of MVUS. The interclass correlation coefficient and Cohen’s kappa analysis were used to analyze the reliability of MVUS (ClinicalTrials.gov:NCT04879173). @*Results@#The area under the curve (AUC) for CD, PD, MVUS, and MVUS-VI was 0.822, 0.844, 0.808, and 0.852 respectively. The optimal cutoff value of the MVUS-VI was 24.95% for distinguishing GD and DT with 87% sensitivity and 80.9% specificity. We found a significant positive correlation of MVUS-VI with thyrotropin receptor antibody (r=0.554) and with thyroid stimulating immunoglobulin bioassay (r=0.841). MVUS showed high intra- and inter-observer reliability from various statistical method. @*Conclusion@#In a real time and quantitative manner, MVUS-VI could be helpful to differentiate GD from thyroiditis in thyrotoxic patients, with less inter-observer variability.
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Base de dados:
WPRIM
Tipo de estudo:
Observational_studies
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article