Frontiers in endocrinology · 2026
Diagnostic value of contrast-enhanced ultrasound combined with other imaging techniques in differentiating benign and malignant thyroid nodules: a network meta-analysis.
СтатьяNetwork Meta-AnalysisСистематический обзорНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Xie Qingying, Yang Zhuoshi, Sun Yue, Zhao Na, Hou Ruirui, Di Zhiyue, Ma Jian
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/myprospero, identifier CRD420251169562.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/myprospero, identifier CRD420251169562.