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Comparison of semi-quantitative scoring and artificial intelligence aided digital image analysis of chromogenic immunohistochemistry

Research output: Contribution to journalArticlepeer-review

János Bencze, Máté Szarka, Balázs Kóti, Woosung Seo, Tibor G. Hortobágyi, Viktor Bencs, László V. Módis, Tibor Hortobágyi

Original languageEnglish
Article number19
JournalBiomolecules
Volume12
Issue number1
Early online date23 Dec 2021
DOIs
E-pub ahead of print23 Dec 2021
PublishedJan 2022

Bibliographical note

Funding Information: Funding: This study was supported by the ÚNKP-21–3 New National Excellence Program of the Ministry of Innovation and Technology from the source of the National Research Development and Innovation Fund; Human Resources Development Operational Programme: EFOP-3.6.3-VEKOP-16-2017-00009 (L.V.M.); Hungarian Brain Research Program (NAP) Grant No. KTIA_13_NAP-A-II/7; SZTE ÁOK-KKA No. 5S 567 (A202); National Research, Development and Innovation Office: NKFIH_SNN_132999 (T.H.). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

King's Authors

Abstract

Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.

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