Automated image analysis in the study of collagenous colitis

Anne-Marie Kanstrup Fiehn,1,2 Martin Kristensson,3 Ulla Engel,4 Lars Kristian Munck,5,6 Susanne Holck,4 Peter Johan Heiberg Engel1,6 1Department of Pathology, Roskilde Hospital, Roskilde, 2Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, 3Digital Pathology, Visiopharm, Hørsholm, 4Department of Pathology, Copenhagen University Hospital, Hvidovre, 5Department of Gastroenterology, Koege Hospital, Koege, 6Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Purpose: The aim of this study was to develop an automated image analysis software to measure the thickness of the subepithelial collagenous band in colon biopsies with collagenous colitis (CC) and incomplete CC (CCi). The software measures the thickness of the collagenous band on microscopic slides stained with Van Gieson (VG). Patients and methods: A training set consisting of ten biopsies diagnosed as CC, CCi, and normal colon mucosa was used to develop the automated image analysis (VG app) to match the assessment by a pathologist. The study set consisted of biopsies from 75 patients. Twenty-five cases were primarily diagnosed as CC, 25 as CCi, and 25 as normal or near-normal colonic mucosa. Four pathologists individually reassessed the biopsies and categorized all into one of the abovementioned three categories. The result of the VG app was correlated with the diagnosis provided by the four pathologists. Results: The interobserver agreement for each pair of pathologists ranged from Κ-values of 0.56–0.81, while the Κ-value for the VG app vs each of the pathologists varied from 0.63 to 0.79. The overall agreement between the four pathologists was Κ=0.69, while the overall agreement between the four pathologists and the VG app was Κ=0.71. Conclusion: In conclusion, the Visiopharm VG app is able to measure the thickness of a subepithelial collagenous band in colon biopsies with an accuracy comparable to the performance of a pathologist and thereby provides a promising supplementary tool for the diagnosis of CC and CCi and in particular for research. Keywords: microscopic colitis, collagenous colitis, incomplete collagenous colitis, automated image analysi