Evidence map›Paper›PMID 40061888›Full record

ArticleJOR spine2025

Application and Validation of Semiautomatic Quantification of Immunohistochemically Stained Sections for Low Cellular Tissue Such as Intervertebral Disc Using QuPath.

Andrea Nüesch, Maria Paola Ferri, Christine L Le Maitre

Abstract read
In one paragraph

Article in JOR spine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

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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

3 authors.

Andrea NüeschDivision of Clinical Medicine, School of Medicine and Population Health University of Sheffield Sheffield UK.ORCID https://orcid.org/0000-0002-0962-8705
Maria Paola FerriBarcelona Supercomputing Center Barcelona Spain.ORCID https://orcid.org/0000-0001-5260-3004
Christine L Le MaitreDivision of Clinical Medicine, School of Medicine and Population Health University of Sheffield Sheffield UK.ORCID https://orcid.org/0000-0003-4489-7107

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Immunohistochemistry (IHC) is a widely used method for localizing and semi-quantifying proteins in tissue samples. Traditional IHC analysis often relies on manually counting 200 cells within a designated area, a time-intensive and subjective process that can compromise reproducibility and accuracy. Advances in digital scanning and bioimage analysis tools, such as the open-source software QuPath, enable semi-automated cell counting, reducing subjectivity and increasing efficiency. Aims: This project developed a QuPath-based script and detailed guide for semi-automatic cell counting, specifically for tissues with low cellularity, such as intervertebral discs and cartilage. Methods and Results: The methodology was validated by demonstrating no significant differences between the manual counting and the semi-automatic quantification ( Discusssion: The approach ensures high reproducibility and accuracy, with reduced variability between raters and laboratories. This semi-automated method is particularly suited for tissues with a high extracellular matrix to cell ratio and low cellularity. By minimizing subjectivity and evaluation time, it provides a robust alternative to manual counting, making it ideal for applications where reproducibility and standardization are critical. While the methodology was effective in low-cellularity tissues, its application in other tissue types warrants further exploration. Conclusions: These findings underscore the potential of QuPath to streamline IHC analysis and enhance inter-laboratory comparability.

Indexed as

image analysisimmunohistochemistryIVDquantificationsemi‐automated

Identifiers

PMID40061888
PMCPMC11885164

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.