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Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring
ABSTRACT: Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositorie...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3379953/ https://www.ncbi.nlm.nih.gov/pubmed/22515559 http://dx.doi.org/10.1186/1746-1596-7-42 |
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author | Rizzardi, Anthony E Johnson, Arthur T Vogel, Rachel Isaksson Pambuccian, Stefan E Henriksen, Jonathan Skubitz, Amy PN Metzger, Gregory J Schmechel, Stephen C |
author_facet | Rizzardi, Anthony E Johnson, Arthur T Vogel, Rachel Isaksson Pambuccian, Stefan E Henriksen, Jonathan Skubitz, Amy PN Metzger, Gregory J Schmechel, Stephen C |
author_sort | Rizzardi, Anthony E |
collection | PubMed |
description | ABSTRACT: Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1649068103671302 |
format | Online Article Text |
id | pubmed-3379953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-33799532012-06-21 Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring Rizzardi, Anthony E Johnson, Arthur T Vogel, Rachel Isaksson Pambuccian, Stefan E Henriksen, Jonathan Skubitz, Amy PN Metzger, Gregory J Schmechel, Stephen C Diagn Pathol Research ABSTRACT: Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1649068103671302 BioMed Central 2012-06-20 /pmc/articles/PMC3379953/ /pubmed/22515559 http://dx.doi.org/10.1186/1746-1596-7-42 Text en Copyright ©2012 Rizzardi et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Rizzardi, Anthony E Johnson, Arthur T Vogel, Rachel Isaksson Pambuccian, Stefan E Henriksen, Jonathan Skubitz, Amy PN Metzger, Gregory J Schmechel, Stephen C Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring |
title | Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring |
title_full | Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring |
title_fullStr | Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring |
title_full_unstemmed | Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring |
title_short | Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring |
title_sort | quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3379953/ https://www.ncbi.nlm.nih.gov/pubmed/22515559 http://dx.doi.org/10.1186/1746-1596-7-42 |
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