Journal of Pathology Informatics Journal of Pathology Informatics
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RESEARCH ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 1  |  Page : 40

QuPath digital immunohistochemical analysis of placental tissue


1 Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
2 Department of Medical Sciences, College of Allied Health Professions, University of Nebraska Medical Center, Omaha, NE, USA
3 Department of Nutrition and Health Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA
4 Division of Biomedical Sciences, School of Medicine, University of California Riverside, Riverside, CA, USA
5 Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
6 Division of Medical Nutrition Education College of Allied Health Professions, University of Nebraska Medical Center, Omaha, NE, USA
7 University of Texas-Southwestern Medical Center, Dallas, TX, USA
8 Department of Pediatrics, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
9 Department of Pediatrics, College of Medicine, University of Michigan, Ann Arbor, MI, USA

Correspondence Address:
Ana G Yuil-Valdes
Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198-3135
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_11_21

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Background: QuPath is an open-source digital image analyzer notable for its user-friendly design, cross-platform compatibility, and customizable functionality. Since it was first released in 2016, at least 624 publications have reported its use, and it has been applied in a wide spectrum of settings. However, there are currently limited reports of its use in placental tissue. Here, we present the use of QuPath to quantify staining of G-protein coupled receptor 18 (GPR18), the receptor for the pro-resolving lipid mediator Resolvin D2, in placental tissue. Methods: Whole slide images of vascular smooth muscle (VSM) and extravillous trophoblast (EVT) cells stained for GPR18 were annotated for areas of interest. Visual scoring was performed on these images by trained and in-training pathologists, while QuPath scoring was performed with the methodology described herein. Results: Bland–Altman analyses showed that, for the VSM category, the two methods were comparable across all staining levels. For EVT cells, the high-intensity staining level was comparable across methods, but the medium and low staining levels were not comparable. Conclusions: Digital image analysis programs offer great potential to revolutionize pathology practice and research by increasing accuracy and decreasing the time and cost of analysis. Careful study is needed to optimize this methodology further.


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