SynthesisBMC pulmonary medicine2025
Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.
Synthesis in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Beyond the primary site: Molecular insights and clinical implications of cancer metastatic overlap between lung and urological organ cancers (Review).Molecular medicine reports · 2026Review
- Artificial Intelligence and Endobronchial Ultrasound for Lymph-Node Detection and Characterization: A Pioneer Multicenter Transatlantic Study.Journal of clinical medicine · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- N1 Staging in Non-Small Cell Lung Cancer: Current Situation, Limitations, and the Importance of Peripheral Nodal Assessment.Cancers · 2026Review
- Applications of Artificial Intelligence in Endobronchial Ultrasound for Lung Cancer Diagnosis and Staging: A Scoping Review.Current oncology (Toronto, Ont.) · 2026Article
- Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.Journal of imaging · 2026Review
- Development and validation of a clinical prediction model for postoperative atrial fibrillation after lung cancer surgery: a machine-learning-based study.Frontiers in surgery · 2026Article
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8 authors.
Funding
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Abstract
backgroundEndobronchial 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.
methodsA 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.
resultsTwelve 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: 4.41-9.08) and 0.28 (95% CI: 0.17-0.47), respectively. The pooled diagnostic OR was 22.38 (95% CI: 11.03-45.38), and the AUROC was 0.90 (95% CI: 0.88-0.93). The subgroup analysis showed higher sensitivity but lower specificity in retrospective studies compared to prospective ones (sensitivity: 0.87 vs. 0.42; specificity: 0.80 vs. 0.93; both p < 0.001). No significant differences were found by lymph node location or AI model type.
conclusionAI-assisted EBUS shows promise in differentiating benign from malignant thoracic LNs, particularly those with high specificity. However, substantial heterogeneity and moderate sensitivity highlight the need for cautious interpretation and further validation. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42025637964.
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