Automated Image Analysis of Lymphocytes in Lymphocytic Colitis
Received Date: Dec 08, 2015 / Accepted Date: Dec 15, 2015 / Published Date: Dec 20, 2015
Abstract
Background and aims: The diagnosis of microscopic colitis (MC) rests on a triad of clinical symptoms, a normal endoscopy and characteristic histopathological findings, among which the number of intraepithelial lymphocytes (IELs) is a determining histopathological factor in diagnosing lymphocytic colitis (LC). When the surface epithelium in a HE stained slide shows a largely increased number of IELs, the diagnosis of LC is easy. However, diagnosing incomplete lymphocytic colitis (LCi) may be difficult as mitotic-and/or apoptotic figures can be hard to rule out. The same goes for distinguishing LCi from LC. The purpose of this study was to address such diagnostic challenges by developing software to count immunostained (CD3) T-lymphocytes of colon biopsies in order to facilitate diagnostics of LC and LCi.
Methods and results: Software for automated image analysis (AIA) was developed using a training set of 10 colon biopsies (LC, LCi and normal) to match manual scorings of IELs in the surface epithelium. The study set consisted of blinded biopsies from 59 patients with LC or LCi in which four pathologists individually gave a diagnosis of LC, LCi or normal colon. The result of AIA was correlated to the diagnosis provided by the 4 pathologists. The overall agreement between AIA and the manual scoring was 96.6% (Cohen’s Kappa: 0.858).
Conclusion: AIA is capable of quantifying CD3 stained lymphocytes in colon biopsies and is applicable as a supplementary diagnostic tool in borderline cases of LC and LCi as well as in research on prospective cohort studies.
Citation: Engel PJH, Fiehn AK, Kristensson M, Engel U, Munck LK, et al. (2015) Automated Image Analysis of Lymphocytes in Lymphocytic Colitis. J Clin Exp Pathol 5:261. Doi: 10.4172/2161-0681.1000261
Copyright: © 2015, Engel PJH, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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