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Update on linear endobronchial ultrasound.

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Current opinion in pulmonary medicine 2026 Vol.32(1) p. 38-45
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Awano N, Izumo T, Nakajima T

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[PURPOSE OF REVIEW] Linear endobronchial ultrasound (EBUS) plays a central role in the diagnosis and mediastinal staging of lung cancer.

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APA Awano N, Izumo T, Nakajima T (2026). Update on linear endobronchial ultrasound.. Current opinion in pulmonary medicine, 32(1), 38-45. https://doi.org/10.1097/MCP.0000000000001227
MLA Awano N, et al.. "Update on linear endobronchial ultrasound.." Current opinion in pulmonary medicine, vol. 32, no. 1, 2026, pp. 38-45.
PMID 41263057 ↗

Abstract

[PURPOSE OF REVIEW] Linear endobronchial ultrasound (EBUS) plays a central role in the diagnosis and mediastinal staging of lung cancer. Recent advancements, including the 9th edition of the International Association for the Study of Lung Cancer (IASLC) TNM staging system and updated guidelines for neoadjuvant therapy, have significantly influenced its clinical utility. This review highlights these key developments and examines the expanding role of EBUS-guided tissue acquisition techniques and rapid on-site evaluation (ROSE).

[RECENT FINDINGS] The IASLC 9th edition offers a more nuanced nodal classification, reinforcing the importance of accurate EBUS-guided lymph node assessment. As neoadjuvant therapies become more common, precise pretreatment staging is critical for treatment planning. Emerging biopsy techniques, such as EBUS-guided intranodal forceps biopsy and cryobiopsy, yield larger tissue samples, improving diagnostic accuracy in complex cases, including lymphoma and benign diseases. Additionally, the integration of artificial intelligence into ROSE enhances the real-time diagnostic performance and mitigates the limitations of traditional cytopathology.

[SUMMARY] EBUS has evolved from diagnostic modality to a cornerstone of personalized lung cancer management. A clear understanding of updated staging systems, therapeutic guidelines, and biopsy strategies is essential. Future research should aim to establish evidence-based diagnostic algorithms incorporating these innovations to optimize patient care.

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