Evidence map›Paper›PMID 35053167›Full record

ArticleBiomolecules2021

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

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

Open access · goldAbstract readComparative Study
In one paragraph

Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 40 citations in OpenAlex.

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  9. Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 4 countries.

János BenczeDivision of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.ORCID 0000-0002-6357-0671
Máté SzarkaHorvath Csaba Laboratory of Bioseparation Sciences, Research Center for Molecular Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.ORCID 0000-0003-2670-5554
Balázs KótiVitrolink Kft., 4033 Debrecen, Hungary.
Woosung SeoDepartment of Surgical Sciences, Radiology, Uppsala University, 751 85 Uppsala, Sweden.
Tibor G HortobágyiInstitute of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, 6725 Szeged, Hungary.
Viktor BencsDepartment of Neurology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
László V MódisDepartment of Behavioural Sciences, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
Tibor HortobágyiELKH-DE Cerebrovascular and Neurodegenerative Research Group, Department of Neurology, University of Debrecen, 4032 Debrecen, Hungary.ORCID 0000-0001-5732-7942
University of Debrecen · HUUppsala University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedImmunohistochemistrySoftwareHumansartificial intelligence (AI)digital image analysisimmunohistochemistrysemi-quantitative scoring

Identifiers

PMID35053167
PMCPMC8774232
OpenAlexW4200214622

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.