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  • 1
    Publication Date: 2018-05-17
    Description: by Entcho Klenske, Steffen Zopf, Clemens Neufert, Andreas Nägel, Jürgen Siebler, Jürgen Gschossmann, Steffen Mühldorfer, Lukas Pfeifer, Sarah Fischer, Francesco Vitali, Marietta Iacucci, Subrata Ghosh, Michelle G. Rath, Peter Klare, Gian E. Tontini, Markus F. Neurath, Timo Rath Background and aims Dye-less chromoendoscopy is an emerging technology for colorectal polyp characterization. Herein, we investigated whether the newly introduced I-scan optical enhancement (OE) can accurately predict polyp histology in vivo in real-time. Methods In this prospective three-phased study, 84 patients with 230 diminutive colorectal polyps were included. During the first two study phases, five endoscopists assessed whether analysis of polyp colour, surface and vascular pattern under i-scan OE can differentiate in vivo between adenomatous and hyperplastic polyps. Finally, junior and experienced endoscopists (JE, EE, each n = 4) not involved in the prior study phases made a post hoc diagnosis of polyp histology using a static i-scan OE image database. Histopathology was used as a gold-standard in all study phases. Results The overall accuracy of i-scan OE for histology prediction was 90% with a sensitivity, specificity, positive (PPV) and negative prediction value (NPV) of 91%, 90%, 86% and 94%, respectively. In high confidence predictions, the diagnostic accuracy increased to 93% with sensitivity, specificity, PPV and NPV of 94%, 91%, 89% and 96%. Colonoscopy surveillance intervals were predicted correctly in ≥ 90% of patients. In the post hoc analysis EE predicted polyp histology under i-scan OE with an overall accuracy of 91%. After a single training session, JE achieved a comparable diagnostic performance for predicting polyp histology with i-scan OE. Conclusion The histology of diminutive colorectal polyps can be accurately predicted with i-scan OE in vivo in real-time. Furthermore, polyp differentiation with i-scan OE appears to require only a short learning curve.
    Electronic ISSN: 1932-6203
    Topics: Medicine , Natural Sciences in General
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