Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis

作者：Fei Tang, Xian-Kui Zha, Wei Ye, Yueming Wang, Yingfeng Wu, Lina Wang, Li-Ping Lyu, Xiaomei Lyu · 发表于：BMC Pulmonary Medicine · 年份：2025 · DOI：10.1186/s12890-025-03760-4 · 被引用次数：6 · 研究领域：Lung Cancer Diagnosis and Treatment、Advanced Radiotherapy Techniques、Tracheal and airway disorders

BACKGROUND: Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging of lung cancer. Artificial intelligence (AI)-assisted EBUS has emerged as a promising tool to enhance diagnostic accuracy. However, its effectiveness in differentiating benign from malignant thoracic LNs remains uncertain. This meta-analysis aimed to evaluate the diagnostic performance of AI-assisted EBUS compared to the pathological reference standards. METHODS: A systematic search was conducted across PubMed, Embase, and Web of Science for studies assessing AI-assisted EBUS in differentiating benign and malignant thoracic LNs. The reference standard included pathological confirmation via EBUS-guided transbronchial needle aspiration, surgical resection, or other histological/cytological validation methods. Sensitivity, specificity, diagnostic likelihood ratios, and diagnostic odds ratio (OR) were pooled using a random-effects model. The area under the receiver operating characteristic curve (AUROC) was summarized to evaluate diagnostic accuracy. Subgroup analyses were conducted by study design, lymph node location, and AI model type. RESULTS: Twelve studies with a total of 6,090 thoracic LNs were included. AI-assisted EBUS showed a pooled sensitivity of 0.75 (95% confidence interval [CI]: 0.60-0.86, I² = 97%) and specificity of 0.88 (95% CI: 0.83-0.92, I² = 96%). The positive and negative likelihood ratios were 6.34 (95% CI: 4.4...