Cargando…

Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Bencze, János, Szarka, Máté, Kóti, Balázs, Seo, Woosung, Hortobágyi, Tibor G., Bencs, Viktor, Módis, László V., Hortobágyi, Tibor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774232/
https://www.ncbi.nlm.nih.gov/pubmed/35053167
http://dx.doi.org/10.3390/biom12010019
_version_ 1784636288202178560
author Bencze, János
Szarka, Máté
Kóti, Balázs
Seo, Woosung
Hortobágyi, Tibor G.
Bencs, Viktor
Módis, László V.
Hortobágyi, Tibor
author_facet Bencze, János
Szarka, Máté
Kóti, Balázs
Seo, Woosung
Hortobágyi, Tibor G.
Bencs, Viktor
Módis, László V.
Hortobágyi, Tibor
author_sort Bencze, János
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8774232
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-87742322022-01-21 Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry Bencze, János Szarka, Máté Kóti, Balázs Seo, Woosung Hortobágyi, Tibor G. Bencs, Viktor Módis, László V. Hortobágyi, Tibor Biomolecules Article 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. MDPI 2021-12-23 /pmc/articles/PMC8774232/ /pubmed/35053167 http://dx.doi.org/10.3390/biom12010019 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bencze, János
Szarka, Máté
Kóti, Balázs
Seo, Woosung
Hortobágyi, Tibor G.
Bencs, Viktor
Módis, László V.
Hortobágyi, Tibor
Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry
title Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry
title_full Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry
title_fullStr Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry
title_full_unstemmed Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry
title_short Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry
title_sort comparison of semi-quantitative scoring and artificial intelligence aided digital image analysis of chromogenic immunohistochemistry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774232/
https://www.ncbi.nlm.nih.gov/pubmed/35053167
http://dx.doi.org/10.3390/biom12010019
work_keys_str_mv AT benczejanos comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry
AT szarkamate comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry
AT kotibalazs comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry
AT seowoosung comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry
AT hortobagyitiborg comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry
AT bencsviktor comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry
AT modislaszlov comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry
AT hortobagyitibor comparisonofsemiquantitativescoringandartificialintelligenceaideddigitalimageanalysisofchromogenicimmunohistochemistry