Li Wanying, Ge Zhitong, Cai Siman, Fang Song, Zhang Min, Wang Hongyan, Li Jianchu
Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Quant Imaging Med Surg. 2025 Jan 2;15(1):440-454. doi: 10.21037/qims-24-1195. Epub 2024 Dec 16.
Superb microvascular imaging (SMI) is an advanced form of Doppler flow imaging which has advantages in tiny vessels and low-speed flow. This study aimed to evaluate the diagnostic performance of combining greyscale ultrasound (US) with SMI in differentiating between benign and malignant thyroid nodules.
A search was conducted in PubMed, Embase, Cochrane Library, Scopus, and Web of Science for relevant studies published till 25 October 2023 that investigated the combined use of greyscale US and SMI to differentiate between benign and malignant thyroid nodules. A subgroup analysis was performed on the basis of different SMI diagnostic criteria for malignant thyroid nodules. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used for quality assessment. The summary sensitivity, specificity, positive likelihood ratio (LR), negative likelihood ratio (LR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve were used for evaluation of diagnostic performance.
A total of 10 original studies, encompassing 1,160 thyroid nodules, 556 of which were malignant, were included in the analysis. The area under the curve (AUC) of greyscale US combined with SMI in diagnosing malignant thyroid nodules was 0.92 [95% confidence interval (CI): 0.89-0.94]. The summary sensitivity and specificity were 0.90 (95% CI: 0.77-0.96) and 0.74 (95% CI: 0.54-0.88) for greyscale US combined with rich vascularity on SMI, 0.86 (95% CI: 0.76-0.92) and 0.83 (95% CI: 0.71-0.91) for greyscale US combined with vascular distribution on SMI, 0.87 (95% CI: 0.80-0.92) and 0.88 (95% CI: 0.81-0.93) for greyscale US combined with the penetrating vessel on SMI, respectively. Meta-regression analysis revealed that variations in sample size could be a source of heterogeneity.
Although the SMI diagnostic criteria for malignant nodules varied among the studies, the combination of greyscale US with SMI demonstrates great diagnostic performance for the differentiation of benign and malignant thyroid nodules. However, more studies are still needed on the standardized SMI diagnostic criteria for thyroid nodules.
超微血管成像（SMI）是一种先进的多普勒血流成像形式，在微小血管和低速血流方面具有优势。本研究旨在评估灰阶超声（US）与SMI联合应用在鉴别甲状腺良恶性结节中的诊断性能。
在PubMed、Embase、Cochrane图书馆、Scopus和Web of Science中进行检索，查找截至2023年10月25日发表的相关研究，这些研究调查了灰阶US与SMI联合应用于鉴别甲状腺良恶性结节的情况。根据不同的甲状腺恶性结节SMI诊断标准进行亚组分析。使用诊断准确性研究质量评估-2（QUADAS-2）进行质量评估。汇总敏感性、特异性、阳性似然比（LR）、阴性似然比（LR）、诊断比值比（DOR）和汇总接收器操作特征（SROC）曲线用于评估诊断性能。
共纳入10项原始研究，包括1160个甲状腺结节，其中556个为恶性结节进行分析。灰阶US联合SMI诊断甲状腺恶性结节时的曲线下面积（AUC）为0.92[95%置信区间（CI）：0.89 - 0.94]。灰阶US联合SMI上丰富血管时的汇总敏感性和特异性分别为0.90（95%CI：0.77 - 0.96）和0.74（95%CI：0.54 - 0.88），灰阶US联合SMI上血管分布时为0.86（95%CI：0.76 - 0.92）和0.83（95%CI：0.71 - 0.91），灰阶US联合SMI上穿支血管时分别为0.87（95%CI：0.80 - 0.92）和0.88（9�%CI：0.81 - 0.93）。Meta回归分析显示样本量的差异可能是异质性的一个来源。
尽管各研究中甲状腺恶性结节的SMI诊断标准不同，但灰阶US与SMI联合应用在鉴别甲状腺良恶性结节方面具有良好的诊断性能。然而，仍需要更多关于甲状腺结节标准化SMI诊断标准的研究。
