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Proceedings Paper

Diagnostic potential of Raman spectroscopy in Barrett's esophagus
Author(s): Louis-Michel Wong Kee Song; Andrea Molckovsky; Kenneth K. Wang; Lawrence J. Burgart; Brion Dolenko; Rajmund L. Somorjai; Brian C. Wilson
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Paper Abstract

Patients with Barrett's esophagus (BE) undergo periodic endoscopic surveillance with random biopsies in an effort to detect dysplastic or early cancerous lesions. Surveillance may be enhanced by near-infrared Raman spectroscopy (NIRS), which has the potential to identify endoscopically-occult dysplastic lesions within the Barrett's segment and allow for targeted biopsies. The aim of this study was to assess the diagnostic performance of NIRS for identifying dysplastic lesions in BE in vivo. Raman spectra (Pexc=70 mW; t=5 s) were collected from Barrett's mucosa at endoscopy using a custom-built NIRS system (λexc=785 nm) equipped with a filtered fiber-optic probe. Each probed site was biopsied for matching histological diagnosis as assessed by an expert pathologist. Diagnostic algorithms were developed using genetic algorithm-based feature selection and linear discriminant analysis, and classification was performed on all spectra with a bootstrap-based cross-validation scheme. The analysis comprised 192 samples (112 non-dysplastic, 54 low-grade dysplasia and 26 high-grade dysplasia/early adenocarcinoma) from 65 patients. Compared with histology, NIRS differentiated dysplastic from non-dysplastic Barrett's samples with 86% sensitivity, 88% specificity and 87% accuracy. NIRS identified 'high-risk' lesions (high-grade dysplasia/early adenocarcinoma) with 88% sensitivity, 89% specificity and 89% accuracy. In the present study, NIRS classified Barrett's epithelia with high and clinically-useful diagnostic accuracy.

Paper Details

Date Published: 1 April 2005
PDF: 7 pages
Proc. SPIE 5692, Advanced Biomedical and Clinical Diagnostic Systems III, (1 April 2005); doi: 10.1117/12.584986
Show Author Affiliations
Louis-Michel Wong Kee Song, Mayo Clinic (United States)
Ontario Cancer Institute/Univ. of Toronto (Canada)
Andrea Molckovsky, Queen's Univ. (Canada)
Kenneth K. Wang, Mayo Clinic (United States)
Lawrence J. Burgart, Mayo Clinic (United States)
Brion Dolenko, National Research Council Canada (Canada)
Rajmund L. Somorjai, National Research Council Canada (Canada)
Brian C. Wilson, Ontario Cancer Institute/Univ. of Toronto (Canada)


Published in SPIE Proceedings Vol. 5692:
Advanced Biomedical and Clinical Diagnostic Systems III
Tuan Vo-Dinh; Warren S. Grundfest; David A. Benaron; Gerald E. Cohn, Editor(s)

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