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

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Jagomast-37-527-541-2022.pdf(7.89 MB)
Date
2022
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Authors
Jagomast, T. ; Idel, C. ; Klapper, L. ; Kuppler, P. ; Proppe, L. ; Beume, S. ; Falougy, M. ; Steller, D. ; Hakim, S.G. ; Offermann, A. ; Roesch, M.C. ; Bruchhage, K.L. ; Perner, S. ; Ribbat Idel, J.
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Publisher
Universidad de Murcia, Departamento de Biologia Celular e Histiologia
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DOI
https://doi.org/10.14670/HH-18-434
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info:eu-repo/semantics/article
Description
Abstract
Objective. Quantifying 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 userdependent 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. Methods. We 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 subcompartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas. Results. Within 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. Conclusion. mDIA 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.
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Segmentation , Immunohistochemistry , Digital pathology , Machine learning , Definiens , QuPath
Citation
Histology and Histopathology Vol. 37, nº6 (2022)
URI
http://hdl.handle.net/10201/128584
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Histology and histopathology Vol.37, nº6 (2022)
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