Diagnostic value of contrast-enhanced ultrasound combined with other imaging techniques in differentiating benign and malignant thyroid nodules: a network meta-analysis.

Authors

Qingying Xie, Zhuoshi Yang, Yue Sun, Na Zhao, Ruirui Hou, Zhiyue Di, Jian Ma

Published in

Frontiers in endocrinology. Volume 17. Pages 1887134. Epub Sep 09, 2026.

Abstract

This network meta-analysis (NMA) intends to fully assess the value of contrast-enhanced ultrasound (CEUS) combined with other imaging techniques in the diagnosis of thyroid nodules (TNs), and provide clinical guidance.
We systematically searched PubMed, Web of Science, Embase, and Cochrane up to October 15, 2025, and assessed study quality by the QUADAS-2 tool. The combination of CEUS and CEUS with other techniques was assessed for accuracy by R4.5.1 and ANOVA-based NMA.
Twenty-six original studies were included, encompassing 5022 TNs from 4484 patients. CEUS-ultrasound elastography (UE) demonstrated favorable diagnostic performance in differentiating benign and malignant TNs (superiority index [SI]: 3.47, 95%CI 0.2-7). The pooled sensitivity (SE) and specificity (SP) of CEUS-UE were 0.92 (95%CI 0.88-0.95, SUCRA 80.0%) and 0.83 (95%CI 0.75-0.89, SUCRA 60.7%), respectively. CEUS-artificial intelligence (AI) was generally effective in identifying high-risk TNs (SI: 3.81, 95%CI 0.33-9), with pooled SE and SP of 0.95 (95%CI 0.91-0.98, SUCRA 90.0%) and 0.81 (95%CI 0.68-0.91, SUCRA 49.6%), respectively. CEUS-conventional ultrasound (US) showed favorable diagnostic performance in identifying non-risk-stratified TNs (SI: 5.29, 95%CI 0.33-9), with pooled SE and SP of 0.89 (95% CI 0.74-0.97, SUCRA 61.4%) and 0.88 (95%CI 0.69-0.97, SUCRA 76.9%), respectively.
CEUS-UE demonstrates favorable overall diagnostic performance in differentiating benign and malignant TNs. CEUS-AI may offer additional value in identifying high-risk TNs with multiple suspicious features. CEUS-US is effective in identifying non-risk-stratified TNs. However, these subgroup findings require further validation in larger, diverse external cohorts.
https://www.crd.york.ac.uk/PROSPERO/myprospero, identifier CRD420251169562.

PMID:
42780335
Bibliographic data and abstract were imported from PubMed on 24 Sep 2026.

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