Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Seoul National University Hospital, Seoul University College of Medicine, Seoul, Republic of Korea.
Medicine (Baltimore). 2022 Feb 4;101(5):e28725. doi: 10.1097/MD.0000000000028725.
This study aimed to investigate the utility of adding superb microvascular imaging (SMI) to B-mode ultrasound (US) for distinguishing between benign and malignant thyroid nodules and evaluate the usefulness of SMI quantification of nodular vascularity for diagnosing thyroid cancer.The malignancy likelihood was scored for 3 datasets before versus after additional color Doppler imaging or SMI using 4-scale visual analysis (i.e., B-mode US alone, B-mode US + color Doppler image, and B-mode US + SMI). Further, the SMI pixel count was measured in the region of interest, including the whole nodule, on the longitudinal view. It was compared between benign and malignant nodules and analyzed according to the US patterns of thyroid nodules based on the Korean thyroid imaging reporting and data system. We calculated the area under the receiver operating characteristic curve values, sensitivities, and specificities.There was no significant difference in the area under the receiver operating characteristic curve values among B-mode, B-mode + color Doppler, and B-mode + SMI. However, the SMI pixel count was significantly higher in malignant thyroid nodules than in benign ones. The optimal cut-off value for the SMI pixel count for predicting malignant thyroid nodules obtained using a receiver operating characteristic curve was 17 (40.54% in sensitivity, 91.3% in specificity). Analysis based on the US pattern of thyroid nodules revealed significant differences in the nodules with low-to-intermediate suspicious US features between malignant and benign nodules.Quantification analysis of vascularity using SMI can differentiate malignant thyroid nodules from benign ones.
本研究旨在探讨在 B 超（US）基础上增加超微血流显像（SMI）对鉴别甲状腺良恶性结节的作用，并评估 SMI 对结节血管分布的定量分析在诊断甲状腺癌中的应用价值。本研究使用 4 分制视觉分析法（即单独 B 超、B 超联合彩色多普勒成像、B 超联合 SMI）对 3 组数据（分别为加入额外的彩色多普勒成像或 SMI 前后）进行良恶性评分。进一步，在长轴切面于感兴趣区测量 SMI 像素计数，包括整个结节。并将其与良恶性结节进行对比，并根据韩国甲状腺影像报告和数据系统（Korean thyroid imaging reporting and data system，K-TIRADS）的甲状腺结节超声模式进行分析。我们计算了受试者工作特征曲线下面积（area under the receiver operating characteristic curve，AUC）值、敏感度和特异度。B 超、B 超联合彩色多普勒、B 超联合 SMI 组间 AUC 值无显著差异。然而，恶性甲状腺结节的 SMI 像素计数明显高于良性结节。通过受试者工作特征曲线获得的预测恶性甲状腺结节的 SMI 像素计数最佳截断值为 17（敏感度为 40.54%，特异度为 91.3%）。基于甲状腺结节超声模式的分析显示，在具有低至中度可疑超声特征的结节中，良恶性结节之间存在显著差异。使用 SMI 对血管分布进行定量分析可区分甲状腺良恶性结节。
