Comparison of Methods to Detect Neoplasia in Patients Undergoing Endoscopic Ultrasound-Guided Fine-Needle Aspiration
Year: 2012
Type: article
Abstract: BACKGROUND & AIMS: Digital image analysis (DIA) and fluorescence in situ hybridization (FISH) can be used to evaluate biliary strictures with greater accuracy than conventional cytology (CC). We performed a prospective evaluation of the accuracy of CC, compared with that of DIA and FISH, in detection of malignancy in patients undergoing endoscopic ultrasonography (EUS) fine-needle aspiration (FNA). METHODS: We collected a minimum of 6 FNA samples from each of 250 patients during EUS. CC or DIA and FISH analyses were performed on every other specimen (from every other FNA pass); patients were randomly assigned to the first test performed. CC slides were reviewed by gastrointestinal cytopathologists who were blinded to all data. Findings from cytohistologic analysis, after a minimum 24-month follow-up period, were used as the standard (n = 202; median age, 65 years). RESULTS: Aspirates were collected from lymph nodes (n = 111), pancreas (n = 61), gastrointestinal lumen wall (n = 9), periluminal mass (n = 4), liver (n = 8), and miscellaneous sites (n = 9). Matched samples provided a mean of 3.2 passes for CC and 1.6 passes for DIA and FISH. The data indicate a potential lack of utility for DIA. The combination of CC and FISH detected malignancy with 11% greater sensitivity than CC alone (P = .0002), but specificity was reduced from 100% to 96%. CONCLUSIONS: FISH analysis identifies neoplastic lesions with significantly greater sensitivity than CC in patients with diverse pathologies who underwent EUS with FNA, despite limited tissue sampling for FISH analysis.
Source: Gastroenterology
Authors: Michael J. Levy, Trynda N. Oberg, Michael B. Campion, Amy C. Clayton, Kevin C. Halling, Michael R. Henry, Benjamin R. Kipp, Thomas J. Sebo, Jun Zhang, Felicity T. Enders, Jonathan E. Clain, Ferga C. Gleeson, Elizabeth Rajan, Lewis R. Roberts, Mark D. Topazian, Kenneth K. Wang, Gregory J. Gores
Institution: Mayo Clinic
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,180
OpenAlex ID: W1973252693
