A96 A QUALITY ASSESSMENT STUDY TO DETERMINE IF TISSUE ACQUISTION AND SPECIMEN HANDLING IMPACT THE DIAGNOSTIC YIELD OF ENDOSCOPIC ULTRASOUND-GUIDED FINE NEEDLE ASPIRATION OF SOLID MASS
S Khan, P Mathura, L Puttangunta, S Girgis, J Zhang, J Nilsson, S Wesilenko

TL;DR
This study examines how tissue collection and handling during endoscopic ultrasound-guided biopsies affect diagnostic accuracy, finding that more needle passes and proper documentation improve results.
Contribution
The study identifies optimal tissue acquisition practices and handling protocols to improve diagnostic yield in EUS-FNAB procedures.
Findings
Diagnostic yield increased with more needle passes, reaching 100% with four passes.
Saline for cell block preparation showed similar diagnostic yield compared to formalin and cytology slides.
Documenting needle passes is recommended as a quality indicator.
Abstract
Endoscopic ultrasound (EUS)-guided fine needle aspiration biopsy (FNAB) of solid mass lesions has a sensitivity and specificity between 50-100%. Tissue acquisition and specimen handling are factors that may contribute to this variability. At our institution, 3 endoscopists perform EUS. The aspirated material obtained is expressed onto slides for cytology (prepared by a nurse) and solid tissue fragments transferred into formalin. At the discretion of the endoscopist, aspirated material is collected in saline for cell block preparation. To assess the impact of tissue collection and specimen handling on diagnostic yield of EUS-FNAB of solid mass lesions. A chart audit was completed for all patients undergoing EUS-FNAB of solid mass lesions between January 1, 2022 and December 31, 2022. Descriptive statistics were completed. A definite diagnosis was considered when calculating the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · Lung Cancer Diagnosis and Treatment · Head and Neck Cancer Studies
