Background: Endobronchial ultrasound (EBUS)-guided transbronchial needle aspiration is a well-established first-line minimally invasive modality for mediastinal lymph node sampling. Although results are excellent overall, the technique underperforms in certain situations. We aimed to describe our results using a new 19-G EBUS-guided transbronchial needle aspiration device to determine safety and feasibility of this approach.
Methods: We completed a retrospective chart review of all cases performed to the time of data analysis at each of 3 study sites.
Results: A total of 165 procedures were performed with a total of 297 individual lymph nodes or lesions sampled with the 19-G device by 10 bronchoscopists. Relatively large targets were selected for sampling with the device (mean lymph node size: 20.4 mm; lung lesions: 33.5 mm). A specific diagnosis was obtained in 77.3% of cases with an additional 13.6% of cases with benign lymphocytes, for a procedural adequacy rate of 90.9%. Procedure sample adequacy was 88.6% in suspected malignant cases, 91.0% in suspected sarcoidosis/lymphadenopathy cases, and 85.7% of cases with suspected lymphoma. On a per-node basis, a specific diagnosis was noted in 191/280 (68.2%) of samples, with an additional 61 showing benign lymphocytes for a per-node sample adequacy rate of 90%. One case (0.6%) of intraprocedure bleeding was noted.
Conclusions: A new flexible 19-G EBUS needle was successfully and safely applied in a large patient cohort for sampling of lung and enlarged mediastinal lesions with high diagnostic rates across clinical indications.
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http://dx.doi.org/10.1097/LBR.0000000000000500 | DOI Listing |
Diagn Cytopathol
January 2025
Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, Missouri, USA.
Background: Endobronchial ultrasound guided Transbronchial Needle Aspiration (EBUS-TBNA) is the predominant method for investigation of centrally located solitary pulmonary nodules. The method is associated with good to excellent diagnostic sensitivity and specificity with the positive predictive value of the test reaching 100% and reported negative predictive values for FNA of pulmonary nodules ranging from 53% to 97%. The impact of correlating cytologic results with imaging and clinical findings for improvement of negative predictive value has been poorly studied.
View Article and Find Full Text PDFRespir Med Case Rep
November 2024
Department of Pulmonary and Critical Care Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA.
Med J Armed Forces India
December 2024
Assistant Professor (Pulmonary Medicine), Command Hospital (Western Command), Chandigarh, India.
Background: The evaluation of mediastinal lymphadenopathy and masses poses a diagnostic challenge because of a myriad of possible etiologic causes; their proximity to numerous vital structures and the difficulty of access for biopsy. Computed tomography is an excellent modality for the initial evaluation of mediastinal lymph nodes (LNs). Tissue diagnosis is of paramount importance to confirm the diagnosis of mediastinal lymphadenopathy.
View Article and Find Full Text PDFCureus
November 2024
Department of Pathology, Okayama University Hospital, Okayama, JPN.
A 71-year-old man with follicular lymphoma of the right inguinal lymph node was referred to our hospital owing to mediastinal lymph node enlargement (left #12). The patient had a history of cyclosporine (CYS-A) and steroid therapy for fibrotic hypersensitivity pneumonitis. Endobronchial ultrasound-transbronchial aspiration and endobronchial ultrasound-guided intranodal forceps biopsy (EBUS-IFB) were performed under conscious sedation using midazolam and fentanyl.
View Article and Find Full Text PDFJTCVS Tech
December 2024
Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.
Objective: Endobronchial ultrasound-guided transbronchial needle aspiration is a vital tool for mediastinal and hilar lymph node staging in patients with lung cancer. Despite its high diagnostic performance and safety, it has a limited negative predictive value. Our objective was to evaluate the diagnostic performance of deep learning-based prediction of lung cancer lymph node metastases using convolutional neural networks developed from automatically extracted images of endobronchial ultrasound videos without supervision of the lymph node location.
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