Background: Conventional transbronchial needle aspiration (C-TBNA) has been proven to be a safe, minimally invasive, and cost-effective technique in establishing the diagnosis of mediastinal pathologies. We studied the success of C-TBNA in our community practice, in patients with mediastinal lymphadenopathies.
Materials And Methods: The technique of C-TBNA was learned solely from the literature, videos, and by practicing on inanimate models during "hands-on" courses. Conventional TBNA with 21- and/or 19-gauge Smooth Shot Needles was performed on consecutive patients with undiagnosed mediastinal lymphadenopathy.
Results: Fifty-four patients (38 men), mean age 56.9±11.8 years, underwent C-TBNA. Thirty-three patients had nodes >20 mm. The final diagnoses were malignancy, 29; sarcoidosis, 9; reactive lymph nodes, 15; and tuberculosis, 1. The final diagnosis was established by C-TBNA in 27. The exclusive diagnostic yield of TBNA was 42.5% (n: 23). Nodal size had an impact on outcome (P=0.002), whereas location did not (P=0.82). C-TBNA was positive in 22/34 when malignancy was suspected (yield 64.7%) and positive in 5/20 when benign diagnoses were also included in the differential (yield 25%) (P=0.005). The sensitivity, specificity, positive predicted value, negative predicted value, and diagnostic accuracy were 79.4%, 100%, 100%, 73%, and 81.5%, respectively.
Conclusions: C-TBNA can be successfully learned without formal training and can be easily applied in the community practice.
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http://dx.doi.org/10.1097/LBR.0b013e31824dd19a | 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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