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Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring
Open accessAbstract
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
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STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/dji237 · Cited by 2,162
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Hydrogels as Drug Delivery Systems: A Review of Current Characterization and Evaluation Techniques
Vigata, M., Meinert, C., Hutmacher, D. W., & Bock, N. (2020) · Pharmaceutics · doi:10.3390/pharmaceutics12121188 · Cited by 544
Principles and approaches for reproducible scoring of tissue stains in research
Meyerholz, D. K., & Beck, A. P. (2018) · Laboratory Investigation · doi:10.1038/s41374-018-0057-0 · Cited by 341
Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_31_18 · Cited by 119
Metadata
- Authors
- Rizzardi, A. E., Johnson, A. T., Vogel, R. I., Pambuccian, S. E., Henriksen, J., Skubitz, A. P., Metzger, G. J., & Schmechel, S. C.
- Venue
- Diagnostic Pathology
- Published
- 2012-06-20
- Volume
- 7
- Issue
- 1
- Pages
- 42-42
- ISSN
- 1746-1596 (ISSN-L)
- OpenAlex
- W2025623420
- Cited by
- 435
- References
- 2
- FWCI
- 27.23
- Type
- journal article
- Language
- en
- Open access
- gold
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- Rizzardi, A. E., Johnson, A. T., Vogel, R. I., Pambuccian, S. E., Henriksen, J., Skubitz, A. P., Metzger, G. J., & Schmechel, S. C. (2012). Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagnostic Pathology. https://doi.org/10.1186/1746-1596-7-42
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