Evidence map›Paper›PMID 35146728›Full record

ArticleHistology and histopathology2022

Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression.

T Jagomast, C Idel, L Klapper, P Kuppler, L Proppe, S Beume, M Falougy, D Steller, S G Hakim, A Offermann and 4 more

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Article in Histology and histopathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

14 authors.

T Jagomast *Institute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany. Tobias.Jagomast@uksh.de.
C Idel *Department of Otorhinolaryngology, University of Luebeck, Luebeck, Germany. Christian.Idel@uksh.de.
L KlapperInstitute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
P KupplerInstitute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
L ProppeDepartment of Gynecology and Obstetrics, University of Luebeck, Luebeck, Germany.
S BeumeDepartment of Gynecology and Obstetrics, University of Luebeck, Luebeck, Germany.
M FalougyDepartment of Oral and Maxillofacial Surgery, University of Luebeck, Luebeck, Germany.
D StellerDepartment of Oral and Maxillofacial Surgery, University of Luebeck, Luebeck, Germany.
S G HakimDepartment of Oral and Maxillofacial Surgery, University of Luebeck, Luebeck, Germany.
A OffermannInstitute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
M C RoeschDepartment of Urology, University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
K L BruchhageDepartment of Otorhinolaryngology, University of Luebeck, Luebeck, Germany.
S Perner *Institute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
J Ribbat-Idel *Institute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.

Funding

clinical scientist fellowship of the medical faculty of the University of Luebeck J26-2018Deutsche Krebshilfe 70113940µBONE program by the Deutsche Forschungsgemeinschaft PE1179/15-1
6 · The paper itself

Abstract

objectiveQuantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a user-dependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems.

methodsWe quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular sub-compartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas.

resultsWithin all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting.

conclusionmDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period.

Indexed as

AlgorithmsImage Processing, Computer-AssistedDiagnostic ImagingHumansProteomicsStaining and Labeling

Identifiers

PMID35146728

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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.